From b714cc658a422cca671e77c3a4b76cbad1d890c6 Mon Sep 17 00:00:00 2001
From: khzadeh <m.khodadadzadeh@utwente.nl>
Date: Wed, 3 May 2023 15:10:05 +0200
Subject: [PATCH 1/5] notebook modelling using recipe data and pycaret

---
 .../mk_model_comparision_pycaret.ipynb        | 919 ++++++++++++++++++
 1 file changed, 919 insertions(+)
 create mode 100644 docs/notebooks/mk_model_comparision_pycaret.ipynb

diff --git a/docs/notebooks/mk_model_comparision_pycaret.ipynb b/docs/notebooks/mk_model_comparision_pycaret.ipynb
new file mode 100644
index 00000000..ecb2a7e2
--- /dev/null
+++ b/docs/notebooks/mk_model_comparision_pycaret.ipynb
@@ -0,0 +1,919 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23ba6ec2-2974-4950-9f69-0b06a4ec5554",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from springtime.main import Workflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "72ed8ef0-0df9-4fca-9be7-200d0d9f685e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recipe = \"/home/jovyan/springtime/src/springtime/recipes/model_comparison_usecase.yaml\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "778938b4-845c-48ce-83b9-6e368413ec93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Dataset npn_obs loaded with 241 rows\n",
+      "Dataset npn_obs resampled to 241 rows\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading dataset:  npn_obs\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2015_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2016_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2017_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2018_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2019_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2020_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "Downloading dataset:  daymet\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Dataset daymet loaded with 326310 rows\n",
+      "Dataset daymet resampled to 894 rows\n",
+      "Datesets joined to shape: (894, 25)\n",
+      "Data saved to: /tmp/output/data.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "Workflow.from_recipe(recipe).execute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d9ea46d3-0240-411f-b359-a84193dfd175",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "64016317-853c-4632-9faf-54bcb31209f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>breaking leaf buds_doy</th>\n",
+       "      <th>tmin_1</th>\n",
+       "      <th>tmin_2</th>\n",
+       "      <th>tmin_3</th>\n",
+       "      <th>tmin_4</th>\n",
+       "      <th>tmin_5</th>\n",
+       "      <th>tmin_6</th>\n",
+       "      <th>tmin_7</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tmax_3</th>\n",
+       "      <th>tmax_4</th>\n",
+       "      <th>tmax_5</th>\n",
+       "      <th>tmax_6</th>\n",
+       "      <th>tmax_7</th>\n",
+       "      <th>tmax_8</th>\n",
+       "      <th>tmax_9</th>\n",
+       "      <th>tmax_10</th>\n",
+       "      <th>tmax_11</th>\n",
+       "      <th>tmax_12</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-122.357971 47.754948)</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3.81</td>\n",
+       "      <td>5.000</td>\n",
+       "      <td>5.59</td>\n",
+       "      <td>4.640</td>\n",
+       "      <td>10.19</td>\n",
+       "      <td>11.625</td>\n",
+       "      <td>14.50</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.26</td>\n",
+       "      <td>14.620</td>\n",
+       "      <td>18.91</td>\n",
+       "      <td>24.785</td>\n",
+       "      <td>25.74</td>\n",
+       "      <td>25.52</td>\n",
+       "      <td>19.165</td>\n",
+       "      <td>17.60</td>\n",
+       "      <td>9.450</td>\n",
+       "      <td>7.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-122.377419 47.776241)</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3.68</td>\n",
+       "      <td>4.955</td>\n",
+       "      <td>5.56</td>\n",
+       "      <td>4.585</td>\n",
+       "      <td>10.11</td>\n",
+       "      <td>11.540</td>\n",
+       "      <td>14.41</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.25</td>\n",
+       "      <td>14.635</td>\n",
+       "      <td>18.96</td>\n",
+       "      <td>24.765</td>\n",
+       "      <td>25.73</td>\n",
+       "      <td>25.58</td>\n",
+       "      <td>19.165</td>\n",
+       "      <td>17.60</td>\n",
+       "      <td>9.475</td>\n",
+       "      <td>7.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-122.185921 47.255966)</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>3.27</td>\n",
+       "      <td>4.915</td>\n",
+       "      <td>5.66</td>\n",
+       "      <td>4.920</td>\n",
+       "      <td>9.83</td>\n",
+       "      <td>11.760</td>\n",
+       "      <td>14.66</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.04</td>\n",
+       "      <td>14.070</td>\n",
+       "      <td>18.19</td>\n",
+       "      <td>25.545</td>\n",
+       "      <td>26.81</td>\n",
+       "      <td>26.20</td>\n",
+       "      <td>18.965</td>\n",
+       "      <td>16.63</td>\n",
+       "      <td>9.120</td>\n",
+       "      <td>6.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-121.861725 47.952686)</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1.93</td>\n",
+       "      <td>3.440</td>\n",
+       "      <td>4.21</td>\n",
+       "      <td>2.920</td>\n",
+       "      <td>9.24</td>\n",
+       "      <td>10.015</td>\n",
+       "      <td>12.76</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.13</td>\n",
+       "      <td>13.765</td>\n",
+       "      <td>17.79</td>\n",
+       "      <td>24.680</td>\n",
+       "      <td>25.72</td>\n",
+       "      <td>25.52</td>\n",
+       "      <td>18.025</td>\n",
+       "      <td>15.80</td>\n",
+       "      <td>7.835</td>\n",
+       "      <td>5.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-122.686279 45.513168)</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>5.290</td>\n",
+       "      <td>6.23</td>\n",
+       "      <td>4.825</td>\n",
+       "      <td>9.91</td>\n",
+       "      <td>12.530</td>\n",
+       "      <td>14.75</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.13</td>\n",
+       "      <td>15.765</td>\n",
+       "      <td>21.45</td>\n",
+       "      <td>28.005</td>\n",
+       "      <td>29.36</td>\n",
+       "      <td>28.66</td>\n",
+       "      <td>22.720</td>\n",
+       "      <td>19.08</td>\n",
+       "      <td>10.715</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year                       geometry  breaking leaf buds_doy  tmin_1  \\\n",
+       "0  2015  POINT (-122.357971 47.754948)                    50.0    3.81   \n",
+       "1  2015  POINT (-122.377419 47.776241)                    50.0    3.68   \n",
+       "2  2015  POINT (-122.185921 47.255966)                    56.0    3.27   \n",
+       "3  2015  POINT (-121.861725 47.952686)                    50.0    1.93   \n",
+       "4  2015  POINT (-122.686279 45.513168)                    99.0    3.70   \n",
+       "\n",
+       "   tmin_2  tmin_3  tmin_4  tmin_5  tmin_6  tmin_7  ...  tmax_3  tmax_4  \\\n",
+       "0   5.000    5.59   4.640   10.19  11.625   14.50  ...   14.26  14.620   \n",
+       "1   4.955    5.56   4.585   10.11  11.540   14.41  ...   14.25  14.635   \n",
+       "2   4.915    5.66   4.920    9.83  11.760   14.66  ...   14.04  14.070   \n",
+       "3   3.440    4.21   2.920    9.24  10.015   12.76  ...   13.13  13.765   \n",
+       "4   5.290    6.23   4.825    9.91  12.530   14.75  ...   17.13  15.765   \n",
+       "\n",
+       "   tmax_5  tmax_6  tmax_7  tmax_8  tmax_9  tmax_10  tmax_11  tmax_12  \n",
+       "0   18.91  24.785   25.74   25.52  19.165    17.60    9.450     7.37  \n",
+       "1   18.96  24.765   25.73   25.58  19.165    17.60    9.475     7.41  \n",
+       "2   18.19  25.545   26.81   26.20  18.965    16.63    9.120     6.62  \n",
+       "3   17.79  24.680   25.72   25.52  18.025    15.80    7.835     5.09  \n",
+       "4   21.45  28.005   29.36   28.66  22.720    19.08   10.715     8.00  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"/tmp/output/data.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "33f238d0-b186-440b-a898-91bd20260e90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(894, 27)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f2db90c6-99fd-4753-95da-e3379f063cde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(241, 27)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(inplace = True)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "01abf19a-63e0-4c6e-adc0-a1b426d82288",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "149"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df.geometry.unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1c671813-fa6e-47ab-90d6-136617fe4f98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import RegressionExperiment and init the class\n",
+    "from pycaret.regression import RegressionExperiment\n",
+    "exp = RegressionExperiment()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c4070825-4166-433e-bb57-47f286e8bf91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_e6514_row9_col1 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_e6514\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_e6514_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
+       "      <th id=\"T_e6514_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_e6514_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
+       "      <td id=\"T_e6514_row0_col1\" class=\"data row0 col1\" >123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_e6514_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
+       "      <td id=\"T_e6514_row1_col1\" class=\"data row1 col1\" >breaking leaf buds_doy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_e6514_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
+       "      <td id=\"T_e6514_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_e6514_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
+       "      <td id=\"T_e6514_row3_col1\" class=\"data row3 col1\" >(241, 27)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_e6514_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
+       "      <td id=\"T_e6514_row4_col1\" class=\"data row4 col1\" >(241, 27)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_e6514_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
+       "      <td id=\"T_e6514_row5_col1\" class=\"data row5 col1\" >(168, 27)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_e6514_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
+       "      <td id=\"T_e6514_row6_col1\" class=\"data row6 col1\" >(73, 27)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_e6514_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
+       "      <td id=\"T_e6514_row7_col1\" class=\"data row7 col1\" >25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_e6514_row8_col0\" class=\"data row8 col0\" >Categorical features</td>\n",
+       "      <td id=\"T_e6514_row8_col1\" class=\"data row8 col1\" >1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_e6514_row9_col0\" class=\"data row9 col0\" >Preprocess</td>\n",
+       "      <td id=\"T_e6514_row9_col1\" class=\"data row9 col1\" >True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_e6514_row10_col0\" class=\"data row10 col0\" >Imputation type</td>\n",
+       "      <td id=\"T_e6514_row10_col1\" class=\"data row10 col1\" >simple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_e6514_row11_col0\" class=\"data row11 col0\" >Numeric imputation</td>\n",
+       "      <td id=\"T_e6514_row11_col1\" class=\"data row11 col1\" >mean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_e6514_row12_col0\" class=\"data row12 col0\" >Categorical imputation</td>\n",
+       "      <td id=\"T_e6514_row12_col1\" class=\"data row12 col1\" >mode</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_e6514_row13_col0\" class=\"data row13 col0\" >Maximum one-hot encoding</td>\n",
+       "      <td id=\"T_e6514_row13_col1\" class=\"data row13 col1\" >25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_e6514_row14_col0\" class=\"data row14 col0\" >Encoding method</td>\n",
+       "      <td id=\"T_e6514_row14_col1\" class=\"data row14 col1\" >None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_e6514_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
+       "      <td id=\"T_e6514_row15_col1\" class=\"data row15 col1\" >KFold</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+       "      <td id=\"T_e6514_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
+       "      <td id=\"T_e6514_row16_col1\" class=\"data row16 col1\" >10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+       "      <td id=\"T_e6514_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
+       "      <td id=\"T_e6514_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+       "      <td id=\"T_e6514_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
+       "      <td id=\"T_e6514_row18_col1\" class=\"data row18 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
+       "      <td id=\"T_e6514_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
+       "      <td id=\"T_e6514_row19_col1\" class=\"data row19 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
+       "      <td id=\"T_e6514_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
+       "      <td id=\"T_e6514_row20_col1\" class=\"data row20 col1\" >reg-default-name</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e6514_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
+       "      <td id=\"T_e6514_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
+       "      <td id=\"T_e6514_row21_col1\" class=\"data row21 col1\" >ab17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f83434a9f30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pycaret.regression.oop.RegressionExperiment at 0x7f84db3c93c0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# init setup on exp\n",
+    "exp.setup(df, target = 'breaking leaf buds_doy', session_id = 123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "edc800e5-5884-4290-9034-38d25aeb75ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_0a04a th {\n",
+       "  text-align: left;\n",
+       "}\n",
+       "#T_0a04a_row0_col0, #T_0a04a_row0_col1, #T_0a04a_row0_col5, #T_0a04a_row0_col6, #T_0a04a_row1_col0, #T_0a04a_row1_col1, #T_0a04a_row1_col2, #T_0a04a_row1_col3, #T_0a04a_row1_col4, #T_0a04a_row1_col6, #T_0a04a_row2_col0, #T_0a04a_row2_col2, #T_0a04a_row2_col3, #T_0a04a_row2_col4, #T_0a04a_row2_col5, #T_0a04a_row3_col0, #T_0a04a_row3_col1, #T_0a04a_row3_col2, #T_0a04a_row3_col3, #T_0a04a_row3_col4, #T_0a04a_row3_col5, #T_0a04a_row3_col6, #T_0a04a_row4_col0, #T_0a04a_row4_col1, #T_0a04a_row4_col2, #T_0a04a_row4_col3, #T_0a04a_row4_col4, #T_0a04a_row4_col5, #T_0a04a_row4_col6, #T_0a04a_row5_col0, #T_0a04a_row5_col1, #T_0a04a_row5_col2, #T_0a04a_row5_col3, #T_0a04a_row5_col4, #T_0a04a_row5_col5, #T_0a04a_row5_col6, #T_0a04a_row6_col0, #T_0a04a_row6_col1, #T_0a04a_row6_col2, #T_0a04a_row6_col3, #T_0a04a_row6_col4, #T_0a04a_row6_col5, #T_0a04a_row6_col6, #T_0a04a_row7_col0, #T_0a04a_row7_col1, #T_0a04a_row7_col2, #T_0a04a_row7_col3, #T_0a04a_row7_col4, #T_0a04a_row7_col5, #T_0a04a_row7_col6, #T_0a04a_row8_col0, #T_0a04a_row8_col1, #T_0a04a_row8_col2, #T_0a04a_row8_col3, #T_0a04a_row8_col4, #T_0a04a_row8_col5, #T_0a04a_row8_col6, #T_0a04a_row9_col0, #T_0a04a_row9_col1, #T_0a04a_row9_col2, #T_0a04a_row9_col3, #T_0a04a_row9_col4, #T_0a04a_row9_col5, #T_0a04a_row9_col6, #T_0a04a_row10_col0, #T_0a04a_row10_col1, #T_0a04a_row10_col2, #T_0a04a_row10_col3, #T_0a04a_row10_col4, #T_0a04a_row10_col5, #T_0a04a_row10_col6, #T_0a04a_row11_col0, #T_0a04a_row11_col1, #T_0a04a_row11_col2, #T_0a04a_row11_col3, #T_0a04a_row11_col4, #T_0a04a_row11_col5, #T_0a04a_row11_col6, #T_0a04a_row12_col0, #T_0a04a_row12_col1, #T_0a04a_row12_col2, #T_0a04a_row12_col3, #T_0a04a_row12_col4, #T_0a04a_row12_col5, #T_0a04a_row12_col6, #T_0a04a_row13_col0, #T_0a04a_row13_col1, #T_0a04a_row13_col2, #T_0a04a_row13_col3, #T_0a04a_row13_col4, #T_0a04a_row13_col5, #T_0a04a_row13_col6, #T_0a04a_row14_col0, #T_0a04a_row14_col1, #T_0a04a_row14_col2, #T_0a04a_row14_col3, #T_0a04a_row14_col4, #T_0a04a_row14_col5, #T_0a04a_row14_col6, #T_0a04a_row15_col0, #T_0a04a_row15_col1, #T_0a04a_row15_col2, #T_0a04a_row15_col3, #T_0a04a_row15_col4, #T_0a04a_row15_col5, #T_0a04a_row15_col6, #T_0a04a_row16_col0, #T_0a04a_row16_col1, #T_0a04a_row16_col2, #T_0a04a_row16_col3, #T_0a04a_row16_col4, #T_0a04a_row16_col5, #T_0a04a_row16_col6, #T_0a04a_row17_col0, #T_0a04a_row17_col1, #T_0a04a_row17_col2, #T_0a04a_row17_col3, #T_0a04a_row17_col4, #T_0a04a_row17_col5, #T_0a04a_row17_col6 {\n",
+       "  text-align: left;\n",
+       "}\n",
+       "#T_0a04a_row0_col2, #T_0a04a_row0_col3, #T_0a04a_row0_col4, #T_0a04a_row1_col5, #T_0a04a_row2_col1, #T_0a04a_row2_col6 {\n",
+       "  text-align: left;\n",
+       "  background-color: yellow;\n",
+       "}\n",
+       "#T_0a04a_row0_col7, #T_0a04a_row1_col7, #T_0a04a_row2_col7, #T_0a04a_row3_col7, #T_0a04a_row4_col7, #T_0a04a_row5_col7, #T_0a04a_row6_col7, #T_0a04a_row7_col7, #T_0a04a_row8_col7, #T_0a04a_row9_col7, #T_0a04a_row10_col7, #T_0a04a_row11_col7, #T_0a04a_row12_col7, #T_0a04a_row13_col7, #T_0a04a_row15_col7, #T_0a04a_row16_col7, #T_0a04a_row17_col7 {\n",
+       "  text-align: left;\n",
+       "  background-color: lightgrey;\n",
+       "}\n",
+       "#T_0a04a_row14_col7 {\n",
+       "  text-align: left;\n",
+       "  background-color: yellow;\n",
+       "  background-color: lightgrey;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_0a04a\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_0a04a_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
+       "      <th id=\"T_0a04a_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
+       "      <th id=\"T_0a04a_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
+       "      <th id=\"T_0a04a_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
+       "      <th id=\"T_0a04a_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
+       "      <th id=\"T_0a04a_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
+       "      <th id=\"T_0a04a_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
+       "      <th id=\"T_0a04a_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row0\" class=\"row_heading level0 row0\" >ada</th>\n",
+       "      <td id=\"T_0a04a_row0_col0\" class=\"data row0 col0\" >AdaBoost Regressor</td>\n",
+       "      <td id=\"T_0a04a_row0_col1\" class=\"data row0 col1\" >26.0191</td>\n",
+       "      <td id=\"T_0a04a_row0_col2\" class=\"data row0 col2\" >1347.3110</td>\n",
+       "      <td id=\"T_0a04a_row0_col3\" class=\"data row0 col3\" >34.1905</td>\n",
+       "      <td id=\"T_0a04a_row0_col4\" class=\"data row0 col4\" >0.8040</td>\n",
+       "      <td id=\"T_0a04a_row0_col5\" class=\"data row0 col5\" >0.4850</td>\n",
+       "      <td id=\"T_0a04a_row0_col6\" class=\"data row0 col6\" >0.4957</td>\n",
+       "      <td id=\"T_0a04a_row0_col7\" class=\"data row0 col7\" >0.0890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row1\" class=\"row_heading level0 row1\" >rf</th>\n",
+       "      <td id=\"T_0a04a_row1_col0\" class=\"data row1 col0\" >Random Forest Regressor</td>\n",
+       "      <td id=\"T_0a04a_row1_col1\" class=\"data row1 col1\" >24.2076</td>\n",
+       "      <td id=\"T_0a04a_row1_col2\" class=\"data row1 col2\" >1444.9782</td>\n",
+       "      <td id=\"T_0a04a_row1_col3\" class=\"data row1 col3\" >35.4898</td>\n",
+       "      <td id=\"T_0a04a_row1_col4\" class=\"data row1 col4\" >0.7894</td>\n",
+       "      <td id=\"T_0a04a_row1_col5\" class=\"data row1 col5\" >0.4643</td>\n",
+       "      <td id=\"T_0a04a_row1_col6\" class=\"data row1 col6\" >0.4128</td>\n",
+       "      <td id=\"T_0a04a_row1_col7\" class=\"data row1 col7\" >0.0890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
+       "      <td id=\"T_0a04a_row2_col0\" class=\"data row2 col0\" >Extra Trees Regressor</td>\n",
+       "      <td id=\"T_0a04a_row2_col1\" class=\"data row2 col1\" >24.0178</td>\n",
+       "      <td id=\"T_0a04a_row2_col2\" class=\"data row2 col2\" >1522.0452</td>\n",
+       "      <td id=\"T_0a04a_row2_col3\" class=\"data row2 col3\" >36.9934</td>\n",
+       "      <td id=\"T_0a04a_row2_col4\" class=\"data row2 col4\" >0.7681</td>\n",
+       "      <td id=\"T_0a04a_row2_col5\" class=\"data row2 col5\" >0.4716</td>\n",
+       "      <td id=\"T_0a04a_row2_col6\" class=\"data row2 col6\" >0.4046</td>\n",
+       "      <td id=\"T_0a04a_row2_col7\" class=\"data row2 col7\" >0.1030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row3\" class=\"row_heading level0 row3\" >gbr</th>\n",
+       "      <td id=\"T_0a04a_row3_col0\" class=\"data row3 col0\" >Gradient Boosting Regressor</td>\n",
+       "      <td id=\"T_0a04a_row3_col1\" class=\"data row3 col1\" >25.3384</td>\n",
+       "      <td id=\"T_0a04a_row3_col2\" class=\"data row3 col2\" >1552.2652</td>\n",
+       "      <td id=\"T_0a04a_row3_col3\" class=\"data row3 col3\" >37.5645</td>\n",
+       "      <td id=\"T_0a04a_row3_col4\" class=\"data row3 col4\" >0.7617</td>\n",
+       "      <td id=\"T_0a04a_row3_col5\" class=\"data row3 col5\" >0.4843</td>\n",
+       "      <td id=\"T_0a04a_row3_col6\" class=\"data row3 col6\" >0.4365</td>\n",
+       "      <td id=\"T_0a04a_row3_col7\" class=\"data row3 col7\" >0.0930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row4\" class=\"row_heading level0 row4\" >lightgbm</th>\n",
+       "      <td id=\"T_0a04a_row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
+       "      <td id=\"T_0a04a_row4_col1\" class=\"data row4 col1\" >27.8242</td>\n",
+       "      <td id=\"T_0a04a_row4_col2\" class=\"data row4 col2\" >1725.6383</td>\n",
+       "      <td id=\"T_0a04a_row4_col3\" class=\"data row4 col3\" >39.9918</td>\n",
+       "      <td id=\"T_0a04a_row4_col4\" class=\"data row4 col4\" >0.7424</td>\n",
+       "      <td id=\"T_0a04a_row4_col5\" class=\"data row4 col5\" >0.5027</td>\n",
+       "      <td id=\"T_0a04a_row4_col6\" class=\"data row4 col6\" >0.4813</td>\n",
+       "      <td id=\"T_0a04a_row4_col7\" class=\"data row4 col7\" >0.1480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n",
+       "      <td id=\"T_0a04a_row5_col0\" class=\"data row5 col0\" >Decision Tree Regressor</td>\n",
+       "      <td id=\"T_0a04a_row5_col1\" class=\"data row5 col1\" >29.3419</td>\n",
+       "      <td id=\"T_0a04a_row5_col2\" class=\"data row5 col2\" >2380.9457</td>\n",
+       "      <td id=\"T_0a04a_row5_col3\" class=\"data row5 col3\" >46.2234</td>\n",
+       "      <td id=\"T_0a04a_row5_col4\" class=\"data row5 col4\" >0.6484</td>\n",
+       "      <td id=\"T_0a04a_row5_col5\" class=\"data row5 col5\" >0.5628</td>\n",
+       "      <td id=\"T_0a04a_row5_col6\" class=\"data row5 col6\" >0.4662</td>\n",
+       "      <td id=\"T_0a04a_row5_col7\" class=\"data row5 col7\" >0.0360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row6\" class=\"row_heading level0 row6\" >br</th>\n",
+       "      <td id=\"T_0a04a_row6_col0\" class=\"data row6 col0\" >Bayesian Ridge</td>\n",
+       "      <td id=\"T_0a04a_row6_col1\" class=\"data row6 col1\" >36.9852</td>\n",
+       "      <td id=\"T_0a04a_row6_col2\" class=\"data row6 col2\" >2402.4154</td>\n",
+       "      <td id=\"T_0a04a_row6_col3\" class=\"data row6 col3\" >47.6191</td>\n",
+       "      <td id=\"T_0a04a_row6_col4\" class=\"data row6 col4\" >0.6420</td>\n",
+       "      <td id=\"T_0a04a_row6_col5\" class=\"data row6 col5\" >0.6107</td>\n",
+       "      <td id=\"T_0a04a_row6_col6\" class=\"data row6 col6\" >0.6385</td>\n",
+       "      <td id=\"T_0a04a_row6_col7\" class=\"data row6 col7\" >0.0360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row7\" class=\"row_heading level0 row7\" >lasso</th>\n",
+       "      <td id=\"T_0a04a_row7_col0\" class=\"data row7 col0\" >Lasso Regression</td>\n",
+       "      <td id=\"T_0a04a_row7_col1\" class=\"data row7 col1\" >36.8419</td>\n",
+       "      <td id=\"T_0a04a_row7_col2\" class=\"data row7 col2\" >2441.5471</td>\n",
+       "      <td id=\"T_0a04a_row7_col3\" class=\"data row7 col3\" >47.8654</td>\n",
+       "      <td id=\"T_0a04a_row7_col4\" class=\"data row7 col4\" >0.6364</td>\n",
+       "      <td id=\"T_0a04a_row7_col5\" class=\"data row7 col5\" >0.5857</td>\n",
+       "      <td id=\"T_0a04a_row7_col6\" class=\"data row7 col6\" >0.6343</td>\n",
+       "      <td id=\"T_0a04a_row7_col7\" class=\"data row7 col7\" >0.3100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row8\" class=\"row_heading level0 row8\" >llar</th>\n",
+       "      <td id=\"T_0a04a_row8_col0\" class=\"data row8 col0\" >Lasso Least Angle Regression</td>\n",
+       "      <td id=\"T_0a04a_row8_col1\" class=\"data row8 col1\" >36.8418</td>\n",
+       "      <td id=\"T_0a04a_row8_col2\" class=\"data row8 col2\" >2441.5185</td>\n",
+       "      <td id=\"T_0a04a_row8_col3\" class=\"data row8 col3\" >47.8649</td>\n",
+       "      <td id=\"T_0a04a_row8_col4\" class=\"data row8 col4\" >0.6364</td>\n",
+       "      <td id=\"T_0a04a_row8_col5\" class=\"data row8 col5\" >0.5857</td>\n",
+       "      <td id=\"T_0a04a_row8_col6\" class=\"data row8 col6\" >0.6343</td>\n",
+       "      <td id=\"T_0a04a_row8_col7\" class=\"data row8 col7\" >0.0490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row9\" class=\"row_heading level0 row9\" >en</th>\n",
+       "      <td id=\"T_0a04a_row9_col0\" class=\"data row9 col0\" >Elastic Net</td>\n",
+       "      <td id=\"T_0a04a_row9_col1\" class=\"data row9 col1\" >41.1054</td>\n",
+       "      <td id=\"T_0a04a_row9_col2\" class=\"data row9 col2\" >2678.6188</td>\n",
+       "      <td id=\"T_0a04a_row9_col3\" class=\"data row9 col3\" >50.5492</td>\n",
+       "      <td id=\"T_0a04a_row9_col4\" class=\"data row9 col4\" >0.6017</td>\n",
+       "      <td id=\"T_0a04a_row9_col5\" class=\"data row9 col5\" >0.6369</td>\n",
+       "      <td id=\"T_0a04a_row9_col6\" class=\"data row9 col6\" >0.7091</td>\n",
+       "      <td id=\"T_0a04a_row9_col7\" class=\"data row9 col7\" >0.2440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row10\" class=\"row_heading level0 row10\" >ridge</th>\n",
+       "      <td id=\"T_0a04a_row10_col0\" class=\"data row10 col0\" >Ridge Regression</td>\n",
+       "      <td id=\"T_0a04a_row10_col1\" class=\"data row10 col1\" >38.6593</td>\n",
+       "      <td id=\"T_0a04a_row10_col2\" class=\"data row10 col2\" >2758.8864</td>\n",
+       "      <td id=\"T_0a04a_row10_col3\" class=\"data row10 col3\" >51.4149</td>\n",
+       "      <td id=\"T_0a04a_row10_col4\" class=\"data row10 col4\" >0.5567</td>\n",
+       "      <td id=\"T_0a04a_row10_col5\" class=\"data row10 col5\" >0.6535</td>\n",
+       "      <td id=\"T_0a04a_row10_col6\" class=\"data row10 col6\" >0.6622</td>\n",
+       "      <td id=\"T_0a04a_row10_col7\" class=\"data row10 col7\" >0.3120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row11\" class=\"row_heading level0 row11\" >lr</th>\n",
+       "      <td id=\"T_0a04a_row11_col0\" class=\"data row11 col0\" >Linear Regression</td>\n",
+       "      <td id=\"T_0a04a_row11_col1\" class=\"data row11 col1\" >39.4589</td>\n",
+       "      <td id=\"T_0a04a_row11_col2\" class=\"data row11 col2\" >2897.5116</td>\n",
+       "      <td id=\"T_0a04a_row11_col3\" class=\"data row11 col3\" >52.6716</td>\n",
+       "      <td id=\"T_0a04a_row11_col4\" class=\"data row11 col4\" >0.5248</td>\n",
+       "      <td id=\"T_0a04a_row11_col5\" class=\"data row11 col5\" >0.6559</td>\n",
+       "      <td id=\"T_0a04a_row11_col6\" class=\"data row11 col6\" >0.6740</td>\n",
+       "      <td id=\"T_0a04a_row11_col7\" class=\"data row11 col7\" >0.5170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row12\" class=\"row_heading level0 row12\" >knn</th>\n",
+       "      <td id=\"T_0a04a_row12_col0\" class=\"data row12 col0\" >K Neighbors Regressor</td>\n",
+       "      <td id=\"T_0a04a_row12_col1\" class=\"data row12 col1\" >33.0965</td>\n",
+       "      <td id=\"T_0a04a_row12_col2\" class=\"data row12 col2\" >2909.5946</td>\n",
+       "      <td id=\"T_0a04a_row12_col3\" class=\"data row12 col3\" >50.8886</td>\n",
+       "      <td id=\"T_0a04a_row12_col4\" class=\"data row12 col4\" >0.5138</td>\n",
+       "      <td id=\"T_0a04a_row12_col5\" class=\"data row12 col5\" >0.5735</td>\n",
+       "      <td id=\"T_0a04a_row12_col6\" class=\"data row12 col6\" >0.6667</td>\n",
+       "      <td id=\"T_0a04a_row12_col7\" class=\"data row12 col7\" >0.0410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row13\" class=\"row_heading level0 row13\" >huber</th>\n",
+       "      <td id=\"T_0a04a_row13_col0\" class=\"data row13 col0\" >Huber Regressor</td>\n",
+       "      <td id=\"T_0a04a_row13_col1\" class=\"data row13 col1\" >41.6265</td>\n",
+       "      <td id=\"T_0a04a_row13_col2\" class=\"data row13 col2\" >3423.0734</td>\n",
+       "      <td id=\"T_0a04a_row13_col3\" class=\"data row13 col3\" >56.2793</td>\n",
+       "      <td id=\"T_0a04a_row13_col4\" class=\"data row13 col4\" >0.4629</td>\n",
+       "      <td id=\"T_0a04a_row13_col5\" class=\"data row13 col5\" >0.6343</td>\n",
+       "      <td id=\"T_0a04a_row13_col6\" class=\"data row13 col6\" >0.7058</td>\n",
+       "      <td id=\"T_0a04a_row13_col7\" class=\"data row13 col7\" >0.0390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row14\" class=\"row_heading level0 row14\" >omp</th>\n",
+       "      <td id=\"T_0a04a_row14_col0\" class=\"data row14 col0\" >Orthogonal Matching Pursuit</td>\n",
+       "      <td id=\"T_0a04a_row14_col1\" class=\"data row14 col1\" >60.7262</td>\n",
+       "      <td id=\"T_0a04a_row14_col2\" class=\"data row14 col2\" >5417.3122</td>\n",
+       "      <td id=\"T_0a04a_row14_col3\" class=\"data row14 col3\" >73.0972</td>\n",
+       "      <td id=\"T_0a04a_row14_col4\" class=\"data row14 col4\" >0.1840</td>\n",
+       "      <td id=\"T_0a04a_row14_col5\" class=\"data row14 col5\" >0.8234</td>\n",
+       "      <td id=\"T_0a04a_row14_col6\" class=\"data row14 col6\" >1.0588</td>\n",
+       "      <td id=\"T_0a04a_row14_col7\" class=\"data row14 col7\" >0.0330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row15\" class=\"row_heading level0 row15\" >dummy</th>\n",
+       "      <td id=\"T_0a04a_row15_col0\" class=\"data row15 col0\" >Dummy Regressor</td>\n",
+       "      <td id=\"T_0a04a_row15_col1\" class=\"data row15 col1\" >65.8218</td>\n",
+       "      <td id=\"T_0a04a_row15_col2\" class=\"data row15 col2\" >7611.0106</td>\n",
+       "      <td id=\"T_0a04a_row15_col3\" class=\"data row15 col3\" >85.7563</td>\n",
+       "      <td id=\"T_0a04a_row15_col4\" class=\"data row15 col4\" >-0.0581</td>\n",
+       "      <td id=\"T_0a04a_row15_col5\" class=\"data row15 col5\" >0.7908</td>\n",
+       "      <td id=\"T_0a04a_row15_col6\" class=\"data row15 col6\" >1.0028</td>\n",
+       "      <td id=\"T_0a04a_row15_col7\" class=\"data row15 col7\" >0.0450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row16\" class=\"row_heading level0 row16\" >par</th>\n",
+       "      <td id=\"T_0a04a_row16_col0\" class=\"data row16 col0\" >Passive Aggressive Regressor</td>\n",
+       "      <td id=\"T_0a04a_row16_col1\" class=\"data row16 col1\" >101.8509</td>\n",
+       "      <td id=\"T_0a04a_row16_col2\" class=\"data row16 col2\" >18325.2595</td>\n",
+       "      <td id=\"T_0a04a_row16_col3\" class=\"data row16 col3\" >125.0586</td>\n",
+       "      <td id=\"T_0a04a_row16_col4\" class=\"data row16 col4\" >-1.8957</td>\n",
+       "      <td id=\"T_0a04a_row16_col5\" class=\"data row16 col5\" >1.0434</td>\n",
+       "      <td id=\"T_0a04a_row16_col6\" class=\"data row16 col6\" >1.8008</td>\n",
+       "      <td id=\"T_0a04a_row16_col7\" class=\"data row16 col7\" >0.0340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a04a_level0_row17\" class=\"row_heading level0 row17\" >lar</th>\n",
+       "      <td id=\"T_0a04a_row17_col0\" class=\"data row17 col0\" >Least Angle Regression</td>\n",
+       "      <td id=\"T_0a04a_row17_col1\" class=\"data row17 col1\" >266.9535</td>\n",
+       "      <td id=\"T_0a04a_row17_col2\" class=\"data row17 col2\" >168513.0474</td>\n",
+       "      <td id=\"T_0a04a_row17_col3\" class=\"data row17 col3\" >344.2414</td>\n",
+       "      <td id=\"T_0a04a_row17_col4\" class=\"data row17 col4\" >-28.9700</td>\n",
+       "      <td id=\"T_0a04a_row17_col5\" class=\"data row17 col5\" >1.4467</td>\n",
+       "      <td id=\"T_0a04a_row17_col6\" class=\"data row17 col6\" >4.3320</td>\n",
+       "      <td id=\"T_0a04a_row17_col7\" class=\"data row17 col7\" >0.0340</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f834ed257b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare baseline models\n",
+    "best = exp.compare_models()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f8455018-e6b6-4e63-b697-4a2137016550",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor(random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor(random_state=123)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "AdaBoostRegressor(random_state=123)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9f6fec2e-c800-4c9d-afce-c1778d4b0ca1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x550 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot residuals\n",
+    "exp.plot_model(best, plot = 'residuals')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "70e04206-e625-452f-a94e-1d6998a4d9d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot error\n",
+    "exp.plot_model(best, plot = 'error')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56cd08f7-d5fa-49ff-849c-08294a025074",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Springtime x86",
+   "language": "python",
+   "name": "springtime_x86"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c5b959dcc13ef2d08e23b4b0d3bdc09d9f50d47e Mon Sep 17 00:00:00 2001
From: khzadeh <m.khodadadzadeh@utwente.nl>
Date: Wed, 3 May 2023 16:52:09 +0200
Subject: [PATCH 2/5] adding a custom function

---
 .../mk_model_comparision_pycaret.ipynb        | 537 ++++++++----------
 1 file changed, 234 insertions(+), 303 deletions(-)

diff --git a/docs/notebooks/mk_model_comparision_pycaret.ipynb b/docs/notebooks/mk_model_comparision_pycaret.ipynb
index ecb2a7e2..82539b74 100644
--- a/docs/notebooks/mk_model_comparision_pycaret.ipynb
+++ b/docs/notebooks/mk_model_comparision_pycaret.ipynb
@@ -330,27 +330,37 @@
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "87377a7c-18ad-4e90-bf8b-ddd44d535b09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns = [\"year\",\"geometry\"], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "01abf19a-63e0-4c6e-adc0-a1b426d82288",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "149"
+       "(241, 25)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(df.geometry.unique())"
+    "df.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "1c671813-fa6e-47ab-90d6-136617fe4f98",
    "metadata": {},
    "outputs": [],
@@ -362,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "c4070825-4166-433e-bb57-47f286e8bf91",
    "metadata": {},
    "outputs": [
@@ -370,134 +380,119 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_e6514_row9_col1 {\n",
+       "#T_8bc65_row8_col1 {\n",
        "  background-color: lightgreen;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_e6514\">\n",
+       "<table id=\"T_8bc65\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_e6514_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
-       "      <th id=\"T_e6514_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
+       "      <th id=\"T_8bc65_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
+       "      <th id=\"T_8bc65_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_e6514_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
-       "      <td id=\"T_e6514_row0_col1\" class=\"data row0 col1\" >123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_e6514_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
-       "      <td id=\"T_e6514_row1_col1\" class=\"data row1 col1\" >breaking leaf buds_doy</td>\n",
+       "      <th id=\"T_8bc65_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_8bc65_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
+       "      <td id=\"T_8bc65_row0_col1\" class=\"data row0 col1\" >123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_e6514_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
-       "      <td id=\"T_e6514_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
+       "      <th id=\"T_8bc65_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_8bc65_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
+       "      <td id=\"T_8bc65_row1_col1\" class=\"data row1 col1\" >breaking leaf buds_doy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_e6514_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
-       "      <td id=\"T_e6514_row3_col1\" class=\"data row3 col1\" >(241, 27)</td>\n",
+       "      <th id=\"T_8bc65_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_8bc65_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
+       "      <td id=\"T_8bc65_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_e6514_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
-       "      <td id=\"T_e6514_row4_col1\" class=\"data row4 col1\" >(241, 27)</td>\n",
+       "      <th id=\"T_8bc65_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_8bc65_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
+       "      <td id=\"T_8bc65_row3_col1\" class=\"data row3 col1\" >(241, 25)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_e6514_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
-       "      <td id=\"T_e6514_row5_col1\" class=\"data row5 col1\" >(168, 27)</td>\n",
+       "      <th id=\"T_8bc65_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_8bc65_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
+       "      <td id=\"T_8bc65_row4_col1\" class=\"data row4 col1\" >(241, 25)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_e6514_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
-       "      <td id=\"T_e6514_row6_col1\" class=\"data row6 col1\" >(73, 27)</td>\n",
+       "      <th id=\"T_8bc65_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_8bc65_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
+       "      <td id=\"T_8bc65_row5_col1\" class=\"data row5 col1\" >(168, 25)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_e6514_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
-       "      <td id=\"T_e6514_row7_col1\" class=\"data row7 col1\" >25</td>\n",
+       "      <th id=\"T_8bc65_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_8bc65_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
+       "      <td id=\"T_8bc65_row6_col1\" class=\"data row6 col1\" >(73, 25)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_e6514_row8_col0\" class=\"data row8 col0\" >Categorical features</td>\n",
-       "      <td id=\"T_e6514_row8_col1\" class=\"data row8 col1\" >1</td>\n",
+       "      <th id=\"T_8bc65_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_8bc65_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
+       "      <td id=\"T_8bc65_row7_col1\" class=\"data row7 col1\" >24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_e6514_row9_col0\" class=\"data row9 col0\" >Preprocess</td>\n",
-       "      <td id=\"T_e6514_row9_col1\" class=\"data row9 col1\" >True</td>\n",
+       "      <th id=\"T_8bc65_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_8bc65_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
+       "      <td id=\"T_8bc65_row8_col1\" class=\"data row8 col1\" >True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "      <td id=\"T_e6514_row10_col0\" class=\"data row10 col0\" >Imputation type</td>\n",
-       "      <td id=\"T_e6514_row10_col1\" class=\"data row10 col1\" >simple</td>\n",
+       "      <th id=\"T_8bc65_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_8bc65_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
+       "      <td id=\"T_8bc65_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "      <td id=\"T_e6514_row11_col0\" class=\"data row11 col0\" >Numeric imputation</td>\n",
-       "      <td id=\"T_e6514_row11_col1\" class=\"data row11 col1\" >mean</td>\n",
+       "      <th id=\"T_8bc65_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_8bc65_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
+       "      <td id=\"T_8bc65_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "      <td id=\"T_e6514_row12_col0\" class=\"data row12 col0\" >Categorical imputation</td>\n",
-       "      <td id=\"T_e6514_row12_col1\" class=\"data row12 col1\" >mode</td>\n",
+       "      <th id=\"T_8bc65_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_8bc65_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
+       "      <td id=\"T_8bc65_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "      <td id=\"T_e6514_row13_col0\" class=\"data row13 col0\" >Maximum one-hot encoding</td>\n",
-       "      <td id=\"T_e6514_row13_col1\" class=\"data row13 col1\" >25</td>\n",
+       "      <th id=\"T_8bc65_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_8bc65_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
+       "      <td id=\"T_8bc65_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
-       "      <td id=\"T_e6514_row14_col0\" class=\"data row14 col0\" >Encoding method</td>\n",
-       "      <td id=\"T_e6514_row14_col1\" class=\"data row14 col1\" >None</td>\n",
+       "      <th id=\"T_8bc65_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_8bc65_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
+       "      <td id=\"T_8bc65_row13_col1\" class=\"data row13 col1\" >10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
-       "      <td id=\"T_e6514_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
-       "      <td id=\"T_e6514_row15_col1\" class=\"data row15 col1\" >KFold</td>\n",
+       "      <th id=\"T_8bc65_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_8bc65_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
+       "      <td id=\"T_8bc65_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
-       "      <td id=\"T_e6514_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
-       "      <td id=\"T_e6514_row16_col1\" class=\"data row16 col1\" >10</td>\n",
+       "      <th id=\"T_8bc65_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_8bc65_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
+       "      <td id=\"T_8bc65_row15_col1\" class=\"data row15 col1\" >False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
-       "      <td id=\"T_e6514_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
-       "      <td id=\"T_e6514_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
+       "      <th id=\"T_8bc65_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+       "      <td id=\"T_8bc65_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
+       "      <td id=\"T_8bc65_row16_col1\" class=\"data row16 col1\" >False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
-       "      <td id=\"T_e6514_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
-       "      <td id=\"T_e6514_row18_col1\" class=\"data row18 col1\" >False</td>\n",
+       "      <th id=\"T_8bc65_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+       "      <td id=\"T_8bc65_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
+       "      <td id=\"T_8bc65_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
-       "      <td id=\"T_e6514_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
-       "      <td id=\"T_e6514_row19_col1\" class=\"data row19 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
-       "      <td id=\"T_e6514_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
-       "      <td id=\"T_e6514_row20_col1\" class=\"data row20 col1\" >reg-default-name</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e6514_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
-       "      <td id=\"T_e6514_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
-       "      <td id=\"T_e6514_row21_col1\" class=\"data row21 col1\" >ab17</td>\n",
+       "      <th id=\"T_8bc65_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+       "      <td id=\"T_8bc65_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
+       "      <td id=\"T_8bc65_row18_col1\" class=\"data row18 col1\" >be5f</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f83434a9f30>"
+       "<pandas.io.formats.style.Styler at 0x7fea576cba00>"
       ]
      },
      "metadata": {},
@@ -506,10 +501,10 @@
     {
      "data": {
       "text/plain": [
-       "<pycaret.regression.oop.RegressionExperiment at 0x7f84db3c93c0>"
+       "<pycaret.regression.oop.RegressionExperiment at 0x7fea62539cf0>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +516,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
+   "id": "fbac3840-009c-4c1c-ab0f-6eced341d83b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVR\n",
+    "svr_rbf = SVR(kernel=\"rbf\", C=100, gamma=0.1, epsilon=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a23a5f25-09f0-45c4-973c-5fc82826d4a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.optimize import curve_fit\n",
+    "from sklearn.base import BaseEstimator, RegressorMixin\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.utils.validation import check_X_y, check_array, check_is_fitted\n",
+    "\n",
+    "# inherit the subclasses\n",
+    "class ExponentialRegressor(BaseEstimator, RegressorMixin):\n",
+    "\n",
+    "  # make the init method\n",
+    "  def __init__(self,maxfev = 3000): \n",
+    "    self.maxfev = maxfev\n",
+    "    return (None)\n",
+    "  \n",
+    "  # make the custom function\n",
+    "  def exp_curve(self,X,*args):\n",
+    "    y_ = np.array(X).dot(np.array(args[1:]))\n",
+    "    y_ = np.exp(y_)\n",
+    "    y = y_*args[0]\n",
+    "    return (y)\n",
+    "  \n",
+    "  # make fit method.\n",
+    "  def fit(self,X,y):\n",
+    "    # check the X & y\n",
+    "    X, y = check_X_y(X, y)\n",
+    "    p0 = np.zeros(X.shape[1]+1)\n",
+    "    self.coefficients_, *_ = curve_fit(self.exp_curve,X,y,p0 = p0)\n",
+    "    # Note that self.coefficients_ has a subsequent \"-\",\n",
+    "    # enough to fullfil the requirement.\n",
+    "    return (None)\n",
+    "  \n",
+    "  # make the predict method.\n",
+    "  def predict(self, X):\n",
+    "    self.predictions = self.exp_curve(X,*self.coefficients_)\n",
+    "    return (self.predictions) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "id": "edc800e5-5884-4290-9034-38d25aeb75ea",
    "metadata": {},
    "outputs": [
@@ -539,244 +589,123 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_0a04a th {\n",
+       "#T_0f3db th {\n",
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-       "#T_0a04a_row0_col0, #T_0a04a_row0_col1, #T_0a04a_row0_col5, #T_0a04a_row0_col6, #T_0a04a_row1_col0, #T_0a04a_row1_col1, #T_0a04a_row1_col2, #T_0a04a_row1_col3, #T_0a04a_row1_col4, #T_0a04a_row1_col6, #T_0a04a_row2_col0, #T_0a04a_row2_col2, #T_0a04a_row2_col3, #T_0a04a_row2_col4, #T_0a04a_row2_col5, #T_0a04a_row3_col0, #T_0a04a_row3_col1, #T_0a04a_row3_col2, #T_0a04a_row3_col3, #T_0a04a_row3_col4, #T_0a04a_row3_col5, #T_0a04a_row3_col6, #T_0a04a_row4_col0, #T_0a04a_row4_col1, #T_0a04a_row4_col2, #T_0a04a_row4_col3, #T_0a04a_row4_col4, #T_0a04a_row4_col5, #T_0a04a_row4_col6, #T_0a04a_row5_col0, #T_0a04a_row5_col1, #T_0a04a_row5_col2, #T_0a04a_row5_col3, #T_0a04a_row5_col4, #T_0a04a_row5_col5, #T_0a04a_row5_col6, #T_0a04a_row6_col0, #T_0a04a_row6_col1, #T_0a04a_row6_col2, #T_0a04a_row6_col3, #T_0a04a_row6_col4, #T_0a04a_row6_col5, #T_0a04a_row6_col6, #T_0a04a_row7_col0, #T_0a04a_row7_col1, #T_0a04a_row7_col2, #T_0a04a_row7_col3, #T_0a04a_row7_col4, #T_0a04a_row7_col5, #T_0a04a_row7_col6, #T_0a04a_row8_col0, #T_0a04a_row8_col1, #T_0a04a_row8_col2, #T_0a04a_row8_col3, #T_0a04a_row8_col4, #T_0a04a_row8_col5, #T_0a04a_row8_col6, #T_0a04a_row9_col0, #T_0a04a_row9_col1, #T_0a04a_row9_col2, #T_0a04a_row9_col3, #T_0a04a_row9_col4, #T_0a04a_row9_col5, #T_0a04a_row9_col6, #T_0a04a_row10_col0, #T_0a04a_row10_col1, #T_0a04a_row10_col2, #T_0a04a_row10_col3, #T_0a04a_row10_col4, #T_0a04a_row10_col5, #T_0a04a_row10_col6, #T_0a04a_row11_col0, #T_0a04a_row11_col1, #T_0a04a_row11_col2, #T_0a04a_row11_col3, #T_0a04a_row11_col4, #T_0a04a_row11_col5, #T_0a04a_row11_col6, #T_0a04a_row12_col0, #T_0a04a_row12_col1, #T_0a04a_row12_col2, #T_0a04a_row12_col3, #T_0a04a_row12_col4, #T_0a04a_row12_col5, #T_0a04a_row12_col6, #T_0a04a_row13_col0, #T_0a04a_row13_col1, #T_0a04a_row13_col2, #T_0a04a_row13_col3, #T_0a04a_row13_col4, #T_0a04a_row13_col5, #T_0a04a_row13_col6, #T_0a04a_row14_col0, #T_0a04a_row14_col1, #T_0a04a_row14_col2, #T_0a04a_row14_col3, #T_0a04a_row14_col4, #T_0a04a_row14_col5, #T_0a04a_row14_col6, #T_0a04a_row15_col0, #T_0a04a_row15_col1, #T_0a04a_row15_col2, #T_0a04a_row15_col3, #T_0a04a_row15_col4, #T_0a04a_row15_col5, #T_0a04a_row15_col6, #T_0a04a_row16_col0, #T_0a04a_row16_col1, #T_0a04a_row16_col2, #T_0a04a_row16_col3, #T_0a04a_row16_col4, #T_0a04a_row16_col5, #T_0a04a_row16_col6, #T_0a04a_row17_col0, #T_0a04a_row17_col1, #T_0a04a_row17_col2, #T_0a04a_row17_col3, #T_0a04a_row17_col4, #T_0a04a_row17_col5, #T_0a04a_row17_col6 {\n",
+       "#T_0f3db_row0_col0, #T_0f3db_row0_col1, #T_0f3db_row0_col2, #T_0f3db_row0_col3, #T_0f3db_row0_col5, #T_0f3db_row0_col6, #T_0f3db_row1_col0, #T_0f3db_row1_col4, #T_0f3db_row1_col6, #T_0f3db_row2_col0, #T_0f3db_row2_col1, #T_0f3db_row2_col2, #T_0f3db_row2_col3, #T_0f3db_row2_col4, #T_0f3db_row2_col5, #T_0f3db_row2_col6, #T_0f3db_row3_col0, #T_0f3db_row3_col1, #T_0f3db_row3_col2, #T_0f3db_row3_col3, #T_0f3db_row3_col4, #T_0f3db_row3_col5, #T_0f3db_row4_col0, #T_0f3db_row4_col1, #T_0f3db_row4_col2, #T_0f3db_row4_col3, #T_0f3db_row4_col4, #T_0f3db_row4_col5, #T_0f3db_row4_col6, #T_0f3db_row5_col0, #T_0f3db_row5_col1, #T_0f3db_row5_col2, #T_0f3db_row5_col3, #T_0f3db_row5_col4, #T_0f3db_row5_col5, #T_0f3db_row5_col6, #T_0f3db_row6_col0, #T_0f3db_row6_col1, #T_0f3db_row6_col2, #T_0f3db_row6_col3, #T_0f3db_row6_col4, #T_0f3db_row6_col5, #T_0f3db_row6_col6 {\n",
        "  text-align: left;\n",
        "}\n",
-       "#T_0a04a_row0_col2, #T_0a04a_row0_col3, #T_0a04a_row0_col4, #T_0a04a_row1_col5, #T_0a04a_row2_col1, #T_0a04a_row2_col6 {\n",
+       "#T_0f3db_row0_col4, #T_0f3db_row1_col1, #T_0f3db_row1_col2, #T_0f3db_row1_col3, #T_0f3db_row1_col5, #T_0f3db_row3_col6 {\n",
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        "  background-color: yellow;\n",
        "}\n",
-       "#T_0a04a_row0_col7, #T_0a04a_row1_col7, #T_0a04a_row2_col7, #T_0a04a_row3_col7, #T_0a04a_row4_col7, #T_0a04a_row5_col7, #T_0a04a_row6_col7, #T_0a04a_row7_col7, #T_0a04a_row8_col7, #T_0a04a_row9_col7, #T_0a04a_row10_col7, #T_0a04a_row11_col7, #T_0a04a_row12_col7, #T_0a04a_row13_col7, #T_0a04a_row15_col7, #T_0a04a_row16_col7, #T_0a04a_row17_col7 {\n",
+       "#T_0f3db_row0_col7, #T_0f3db_row1_col7, #T_0f3db_row3_col7, #T_0f3db_row4_col7, #T_0f3db_row5_col7, #T_0f3db_row6_col7 {\n",
        "  text-align: left;\n",
        "  background-color: lightgrey;\n",
        "}\n",
-       "#T_0a04a_row14_col7 {\n",
+       "#T_0f3db_row2_col7 {\n",
        "  text-align: left;\n",
        "  background-color: yellow;\n",
        "  background-color: lightgrey;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_0a04a\">\n",
+       "<table id=\"T_0f3db\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_0a04a_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
-       "      <th id=\"T_0a04a_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
-       "      <th id=\"T_0a04a_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
-       "      <th id=\"T_0a04a_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
-       "      <th id=\"T_0a04a_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
-       "      <th id=\"T_0a04a_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
-       "      <th id=\"T_0a04a_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
-       "      <th id=\"T_0a04a_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
+       "      <th id=\"T_0f3db_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
+       "      <th id=\"T_0f3db_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
+       "      <th id=\"T_0f3db_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
+       "      <th id=\"T_0f3db_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
+       "      <th id=\"T_0f3db_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
+       "      <th id=\"T_0f3db_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
+       "      <th id=\"T_0f3db_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
+       "      <th id=\"T_0f3db_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row0\" class=\"row_heading level0 row0\" >ada</th>\n",
-       "      <td id=\"T_0a04a_row0_col0\" class=\"data row0 col0\" >AdaBoost Regressor</td>\n",
-       "      <td id=\"T_0a04a_row0_col1\" class=\"data row0 col1\" >26.0191</td>\n",
-       "      <td id=\"T_0a04a_row0_col2\" class=\"data row0 col2\" >1347.3110</td>\n",
-       "      <td id=\"T_0a04a_row0_col3\" class=\"data row0 col3\" >34.1905</td>\n",
-       "      <td id=\"T_0a04a_row0_col4\" class=\"data row0 col4\" >0.8040</td>\n",
-       "      <td id=\"T_0a04a_row0_col5\" class=\"data row0 col5\" >0.4850</td>\n",
-       "      <td id=\"T_0a04a_row0_col6\" class=\"data row0 col6\" >0.4957</td>\n",
-       "      <td id=\"T_0a04a_row0_col7\" class=\"data row0 col7\" >0.0890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row1\" class=\"row_heading level0 row1\" >rf</th>\n",
-       "      <td id=\"T_0a04a_row1_col0\" class=\"data row1 col0\" >Random Forest Regressor</td>\n",
-       "      <td id=\"T_0a04a_row1_col1\" class=\"data row1 col1\" >24.2076</td>\n",
-       "      <td id=\"T_0a04a_row1_col2\" class=\"data row1 col2\" >1444.9782</td>\n",
-       "      <td id=\"T_0a04a_row1_col3\" class=\"data row1 col3\" >35.4898</td>\n",
-       "      <td id=\"T_0a04a_row1_col4\" class=\"data row1 col4\" >0.7894</td>\n",
-       "      <td id=\"T_0a04a_row1_col5\" class=\"data row1 col5\" >0.4643</td>\n",
-       "      <td id=\"T_0a04a_row1_col6\" class=\"data row1 col6\" >0.4128</td>\n",
-       "      <td id=\"T_0a04a_row1_col7\" class=\"data row1 col7\" >0.0890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
-       "      <td id=\"T_0a04a_row2_col0\" class=\"data row2 col0\" >Extra Trees Regressor</td>\n",
-       "      <td id=\"T_0a04a_row2_col1\" class=\"data row2 col1\" >24.0178</td>\n",
-       "      <td id=\"T_0a04a_row2_col2\" class=\"data row2 col2\" >1522.0452</td>\n",
-       "      <td id=\"T_0a04a_row2_col3\" class=\"data row2 col3\" >36.9934</td>\n",
-       "      <td id=\"T_0a04a_row2_col4\" class=\"data row2 col4\" >0.7681</td>\n",
-       "      <td id=\"T_0a04a_row2_col5\" class=\"data row2 col5\" >0.4716</td>\n",
-       "      <td id=\"T_0a04a_row2_col6\" class=\"data row2 col6\" >0.4046</td>\n",
-       "      <td id=\"T_0a04a_row2_col7\" class=\"data row2 col7\" >0.1030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row3\" class=\"row_heading level0 row3\" >gbr</th>\n",
-       "      <td id=\"T_0a04a_row3_col0\" class=\"data row3 col0\" >Gradient Boosting Regressor</td>\n",
-       "      <td id=\"T_0a04a_row3_col1\" class=\"data row3 col1\" >25.3384</td>\n",
-       "      <td id=\"T_0a04a_row3_col2\" class=\"data row3 col2\" >1552.2652</td>\n",
-       "      <td id=\"T_0a04a_row3_col3\" class=\"data row3 col3\" >37.5645</td>\n",
-       "      <td id=\"T_0a04a_row3_col4\" class=\"data row3 col4\" >0.7617</td>\n",
-       "      <td id=\"T_0a04a_row3_col5\" class=\"data row3 col5\" >0.4843</td>\n",
-       "      <td id=\"T_0a04a_row3_col6\" class=\"data row3 col6\" >0.4365</td>\n",
-       "      <td id=\"T_0a04a_row3_col7\" class=\"data row3 col7\" >0.0930</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row4\" class=\"row_heading level0 row4\" >lightgbm</th>\n",
-       "      <td id=\"T_0a04a_row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
-       "      <td id=\"T_0a04a_row4_col1\" class=\"data row4 col1\" >27.8242</td>\n",
-       "      <td id=\"T_0a04a_row4_col2\" class=\"data row4 col2\" >1725.6383</td>\n",
-       "      <td id=\"T_0a04a_row4_col3\" class=\"data row4 col3\" >39.9918</td>\n",
-       "      <td id=\"T_0a04a_row4_col4\" class=\"data row4 col4\" >0.7424</td>\n",
-       "      <td id=\"T_0a04a_row4_col5\" class=\"data row4 col5\" >0.5027</td>\n",
-       "      <td id=\"T_0a04a_row4_col6\" class=\"data row4 col6\" >0.4813</td>\n",
-       "      <td id=\"T_0a04a_row4_col7\" class=\"data row4 col7\" >0.1480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n",
-       "      <td id=\"T_0a04a_row5_col0\" class=\"data row5 col0\" >Decision Tree Regressor</td>\n",
-       "      <td id=\"T_0a04a_row5_col1\" class=\"data row5 col1\" >29.3419</td>\n",
-       "      <td id=\"T_0a04a_row5_col2\" class=\"data row5 col2\" >2380.9457</td>\n",
-       "      <td id=\"T_0a04a_row5_col3\" class=\"data row5 col3\" >46.2234</td>\n",
-       "      <td id=\"T_0a04a_row5_col4\" class=\"data row5 col4\" >0.6484</td>\n",
-       "      <td id=\"T_0a04a_row5_col5\" class=\"data row5 col5\" >0.5628</td>\n",
-       "      <td id=\"T_0a04a_row5_col6\" class=\"data row5 col6\" >0.4662</td>\n",
-       "      <td id=\"T_0a04a_row5_col7\" class=\"data row5 col7\" >0.0360</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row6\" class=\"row_heading level0 row6\" >br</th>\n",
-       "      <td id=\"T_0a04a_row6_col0\" class=\"data row6 col0\" >Bayesian Ridge</td>\n",
-       "      <td id=\"T_0a04a_row6_col1\" class=\"data row6 col1\" >36.9852</td>\n",
-       "      <td id=\"T_0a04a_row6_col2\" class=\"data row6 col2\" >2402.4154</td>\n",
-       "      <td id=\"T_0a04a_row6_col3\" class=\"data row6 col3\" >47.6191</td>\n",
-       "      <td id=\"T_0a04a_row6_col4\" class=\"data row6 col4\" >0.6420</td>\n",
-       "      <td id=\"T_0a04a_row6_col5\" class=\"data row6 col5\" >0.6107</td>\n",
-       "      <td id=\"T_0a04a_row6_col6\" class=\"data row6 col6\" >0.6385</td>\n",
-       "      <td id=\"T_0a04a_row6_col7\" class=\"data row6 col7\" >0.0360</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row7\" class=\"row_heading level0 row7\" >lasso</th>\n",
-       "      <td id=\"T_0a04a_row7_col0\" class=\"data row7 col0\" >Lasso Regression</td>\n",
-       "      <td id=\"T_0a04a_row7_col1\" class=\"data row7 col1\" >36.8419</td>\n",
-       "      <td id=\"T_0a04a_row7_col2\" class=\"data row7 col2\" >2441.5471</td>\n",
-       "      <td id=\"T_0a04a_row7_col3\" class=\"data row7 col3\" >47.8654</td>\n",
-       "      <td id=\"T_0a04a_row7_col4\" class=\"data row7 col4\" >0.6364</td>\n",
-       "      <td id=\"T_0a04a_row7_col5\" class=\"data row7 col5\" >0.5857</td>\n",
-       "      <td id=\"T_0a04a_row7_col6\" class=\"data row7 col6\" >0.6343</td>\n",
-       "      <td id=\"T_0a04a_row7_col7\" class=\"data row7 col7\" >0.3100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row8\" class=\"row_heading level0 row8\" >llar</th>\n",
-       "      <td id=\"T_0a04a_row8_col0\" class=\"data row8 col0\" >Lasso Least Angle Regression</td>\n",
-       "      <td id=\"T_0a04a_row8_col1\" class=\"data row8 col1\" >36.8418</td>\n",
-       "      <td id=\"T_0a04a_row8_col2\" class=\"data row8 col2\" >2441.5185</td>\n",
-       "      <td id=\"T_0a04a_row8_col3\" class=\"data row8 col3\" >47.8649</td>\n",
-       "      <td id=\"T_0a04a_row8_col4\" class=\"data row8 col4\" >0.6364</td>\n",
-       "      <td id=\"T_0a04a_row8_col5\" class=\"data row8 col5\" >0.5857</td>\n",
-       "      <td id=\"T_0a04a_row8_col6\" class=\"data row8 col6\" >0.6343</td>\n",
-       "      <td id=\"T_0a04a_row8_col7\" class=\"data row8 col7\" >0.0490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row9\" class=\"row_heading level0 row9\" >en</th>\n",
-       "      <td id=\"T_0a04a_row9_col0\" class=\"data row9 col0\" >Elastic Net</td>\n",
-       "      <td id=\"T_0a04a_row9_col1\" class=\"data row9 col1\" >41.1054</td>\n",
-       "      <td id=\"T_0a04a_row9_col2\" class=\"data row9 col2\" >2678.6188</td>\n",
-       "      <td id=\"T_0a04a_row9_col3\" class=\"data row9 col3\" >50.5492</td>\n",
-       "      <td id=\"T_0a04a_row9_col4\" class=\"data row9 col4\" >0.6017</td>\n",
-       "      <td id=\"T_0a04a_row9_col5\" class=\"data row9 col5\" >0.6369</td>\n",
-       "      <td id=\"T_0a04a_row9_col6\" class=\"data row9 col6\" >0.7091</td>\n",
-       "      <td id=\"T_0a04a_row9_col7\" class=\"data row9 col7\" >0.2440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row10\" class=\"row_heading level0 row10\" >ridge</th>\n",
-       "      <td id=\"T_0a04a_row10_col0\" class=\"data row10 col0\" >Ridge Regression</td>\n",
-       "      <td id=\"T_0a04a_row10_col1\" class=\"data row10 col1\" >38.6593</td>\n",
-       "      <td id=\"T_0a04a_row10_col2\" class=\"data row10 col2\" >2758.8864</td>\n",
-       "      <td id=\"T_0a04a_row10_col3\" class=\"data row10 col3\" >51.4149</td>\n",
-       "      <td id=\"T_0a04a_row10_col4\" class=\"data row10 col4\" >0.5567</td>\n",
-       "      <td id=\"T_0a04a_row10_col5\" class=\"data row10 col5\" >0.6535</td>\n",
-       "      <td id=\"T_0a04a_row10_col6\" class=\"data row10 col6\" >0.6622</td>\n",
-       "      <td id=\"T_0a04a_row10_col7\" class=\"data row10 col7\" >0.3120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row11\" class=\"row_heading level0 row11\" >lr</th>\n",
-       "      <td id=\"T_0a04a_row11_col0\" class=\"data row11 col0\" >Linear Regression</td>\n",
-       "      <td id=\"T_0a04a_row11_col1\" class=\"data row11 col1\" >39.4589</td>\n",
-       "      <td id=\"T_0a04a_row11_col2\" class=\"data row11 col2\" >2897.5116</td>\n",
-       "      <td id=\"T_0a04a_row11_col3\" class=\"data row11 col3\" >52.6716</td>\n",
-       "      <td id=\"T_0a04a_row11_col4\" class=\"data row11 col4\" >0.5248</td>\n",
-       "      <td id=\"T_0a04a_row11_col5\" class=\"data row11 col5\" >0.6559</td>\n",
-       "      <td id=\"T_0a04a_row11_col6\" class=\"data row11 col6\" >0.6740</td>\n",
-       "      <td id=\"T_0a04a_row11_col7\" class=\"data row11 col7\" >0.5170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row12\" class=\"row_heading level0 row12\" >knn</th>\n",
-       "      <td id=\"T_0a04a_row12_col0\" class=\"data row12 col0\" >K Neighbors Regressor</td>\n",
-       "      <td id=\"T_0a04a_row12_col1\" class=\"data row12 col1\" >33.0965</td>\n",
-       "      <td id=\"T_0a04a_row12_col2\" class=\"data row12 col2\" >2909.5946</td>\n",
-       "      <td id=\"T_0a04a_row12_col3\" class=\"data row12 col3\" >50.8886</td>\n",
-       "      <td id=\"T_0a04a_row12_col4\" class=\"data row12 col4\" >0.5138</td>\n",
-       "      <td id=\"T_0a04a_row12_col5\" class=\"data row12 col5\" >0.5735</td>\n",
-       "      <td id=\"T_0a04a_row12_col6\" class=\"data row12 col6\" >0.6667</td>\n",
-       "      <td id=\"T_0a04a_row12_col7\" class=\"data row12 col7\" >0.0410</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row13\" class=\"row_heading level0 row13\" >huber</th>\n",
-       "      <td id=\"T_0a04a_row13_col0\" class=\"data row13 col0\" >Huber Regressor</td>\n",
-       "      <td id=\"T_0a04a_row13_col1\" class=\"data row13 col1\" >41.6265</td>\n",
-       "      <td id=\"T_0a04a_row13_col2\" class=\"data row13 col2\" >3423.0734</td>\n",
-       "      <td id=\"T_0a04a_row13_col3\" class=\"data row13 col3\" >56.2793</td>\n",
-       "      <td id=\"T_0a04a_row13_col4\" class=\"data row13 col4\" >0.4629</td>\n",
-       "      <td id=\"T_0a04a_row13_col5\" class=\"data row13 col5\" >0.6343</td>\n",
-       "      <td id=\"T_0a04a_row13_col6\" class=\"data row13 col6\" >0.7058</td>\n",
-       "      <td id=\"T_0a04a_row13_col7\" class=\"data row13 col7\" >0.0390</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row14\" class=\"row_heading level0 row14\" >omp</th>\n",
-       "      <td id=\"T_0a04a_row14_col0\" class=\"data row14 col0\" >Orthogonal Matching Pursuit</td>\n",
-       "      <td id=\"T_0a04a_row14_col1\" class=\"data row14 col1\" >60.7262</td>\n",
-       "      <td id=\"T_0a04a_row14_col2\" class=\"data row14 col2\" >5417.3122</td>\n",
-       "      <td id=\"T_0a04a_row14_col3\" class=\"data row14 col3\" >73.0972</td>\n",
-       "      <td id=\"T_0a04a_row14_col4\" class=\"data row14 col4\" >0.1840</td>\n",
-       "      <td id=\"T_0a04a_row14_col5\" class=\"data row14 col5\" >0.8234</td>\n",
-       "      <td id=\"T_0a04a_row14_col6\" class=\"data row14 col6\" >1.0588</td>\n",
-       "      <td id=\"T_0a04a_row14_col7\" class=\"data row14 col7\" >0.0330</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row15\" class=\"row_heading level0 row15\" >dummy</th>\n",
-       "      <td id=\"T_0a04a_row15_col0\" class=\"data row15 col0\" >Dummy Regressor</td>\n",
-       "      <td id=\"T_0a04a_row15_col1\" class=\"data row15 col1\" >65.8218</td>\n",
-       "      <td id=\"T_0a04a_row15_col2\" class=\"data row15 col2\" >7611.0106</td>\n",
-       "      <td id=\"T_0a04a_row15_col3\" class=\"data row15 col3\" >85.7563</td>\n",
-       "      <td id=\"T_0a04a_row15_col4\" class=\"data row15 col4\" >-0.0581</td>\n",
-       "      <td id=\"T_0a04a_row15_col5\" class=\"data row15 col5\" >0.7908</td>\n",
-       "      <td id=\"T_0a04a_row15_col6\" class=\"data row15 col6\" >1.0028</td>\n",
-       "      <td id=\"T_0a04a_row15_col7\" class=\"data row15 col7\" >0.0450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row16\" class=\"row_heading level0 row16\" >par</th>\n",
-       "      <td id=\"T_0a04a_row16_col0\" class=\"data row16 col0\" >Passive Aggressive Regressor</td>\n",
-       "      <td id=\"T_0a04a_row16_col1\" class=\"data row16 col1\" >101.8509</td>\n",
-       "      <td id=\"T_0a04a_row16_col2\" class=\"data row16 col2\" >18325.2595</td>\n",
-       "      <td id=\"T_0a04a_row16_col3\" class=\"data row16 col3\" >125.0586</td>\n",
-       "      <td id=\"T_0a04a_row16_col4\" class=\"data row16 col4\" >-1.8957</td>\n",
-       "      <td id=\"T_0a04a_row16_col5\" class=\"data row16 col5\" >1.0434</td>\n",
-       "      <td id=\"T_0a04a_row16_col6\" class=\"data row16 col6\" >1.8008</td>\n",
-       "      <td id=\"T_0a04a_row16_col7\" class=\"data row16 col7\" >0.0340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0a04a_level0_row17\" class=\"row_heading level0 row17\" >lar</th>\n",
-       "      <td id=\"T_0a04a_row17_col0\" class=\"data row17 col0\" >Least Angle Regression</td>\n",
-       "      <td id=\"T_0a04a_row17_col1\" class=\"data row17 col1\" >266.9535</td>\n",
-       "      <td id=\"T_0a04a_row17_col2\" class=\"data row17 col2\" >168513.0474</td>\n",
-       "      <td id=\"T_0a04a_row17_col3\" class=\"data row17 col3\" >344.2414</td>\n",
-       "      <td id=\"T_0a04a_row17_col4\" class=\"data row17 col4\" >-28.9700</td>\n",
-       "      <td id=\"T_0a04a_row17_col5\" class=\"data row17 col5\" >1.4467</td>\n",
-       "      <td id=\"T_0a04a_row17_col6\" class=\"data row17 col6\" >4.3320</td>\n",
-       "      <td id=\"T_0a04a_row17_col7\" class=\"data row17 col7\" >0.0340</td>\n",
+       "      <th id=\"T_0f3db_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_0f3db_row0_col0\" class=\"data row0 col0\" >AdaBoost Regressor</td>\n",
+       "      <td id=\"T_0f3db_row0_col1\" class=\"data row0 col1\" >26.6921</td>\n",
+       "      <td id=\"T_0f3db_row0_col2\" class=\"data row0 col2\" >1470.7824</td>\n",
+       "      <td id=\"T_0f3db_row0_col3\" class=\"data row0 col3\" >36.1062</td>\n",
+       "      <td id=\"T_0f3db_row0_col4\" class=\"data row0 col4\" >0.7910</td>\n",
+       "      <td id=\"T_0f3db_row0_col5\" class=\"data row0 col5\" >0.4942</td>\n",
+       "      <td id=\"T_0f3db_row0_col6\" class=\"data row0 col6\" >0.4973</td>\n",
+       "      <td id=\"T_0f3db_row0_col7\" class=\"data row0 col7\" >0.3000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0f3db_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
+       "      <td id=\"T_0f3db_row1_col0\" class=\"data row1 col0\" >Random Forest Regressor</td>\n",
+       "      <td id=\"T_0f3db_row1_col1\" class=\"data row1 col1\" >24.5937</td>\n",
+       "      <td id=\"T_0f3db_row1_col2\" class=\"data row1 col2\" >1467.8504</td>\n",
+       "      <td id=\"T_0f3db_row1_col3\" class=\"data row1 col3\" >35.9053</td>\n",
+       "      <td id=\"T_0f3db_row1_col4\" class=\"data row1 col4\" >0.7857</td>\n",
+       "      <td id=\"T_0f3db_row1_col5\" class=\"data row1 col5\" >0.4724</td>\n",
+       "      <td id=\"T_0f3db_row1_col6\" class=\"data row1 col6\" >0.4236</td>\n",
+       "      <td id=\"T_0f3db_row1_col7\" class=\"data row1 col7\" >0.3810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0f3db_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
+       "      <td id=\"T_0f3db_row2_col0\" class=\"data row2 col0\" >Support Vector Regression</td>\n",
+       "      <td id=\"T_0f3db_row2_col1\" class=\"data row2 col1\" >26.9678</td>\n",
+       "      <td id=\"T_0f3db_row2_col2\" class=\"data row2 col2\" >1684.4732</td>\n",
+       "      <td id=\"T_0f3db_row2_col3\" class=\"data row2 col3\" >39.4120</td>\n",
+       "      <td id=\"T_0f3db_row2_col4\" class=\"data row2 col4\" >0.7467</td>\n",
+       "      <td id=\"T_0f3db_row2_col5\" class=\"data row2 col5\" >0.5089</td>\n",
+       "      <td id=\"T_0f3db_row2_col6\" class=\"data row2 col6\" >0.4589</td>\n",
+       "      <td id=\"T_0f3db_row2_col7\" class=\"data row2 col7\" >0.0220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0f3db_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
+       "      <td id=\"T_0f3db_row3_col0\" class=\"data row3 col0\" >Decision Tree Regressor</td>\n",
+       "      <td id=\"T_0f3db_row3_col1\" class=\"data row3 col1\" >26.0720</td>\n",
+       "      <td id=\"T_0f3db_row3_col2\" class=\"data row3 col2\" >1761.6493</td>\n",
+       "      <td id=\"T_0f3db_row3_col3\" class=\"data row3 col3\" >39.8640</td>\n",
+       "      <td id=\"T_0f3db_row3_col4\" class=\"data row3 col4\" >0.7286</td>\n",
+       "      <td id=\"T_0f3db_row3_col5\" class=\"data row3 col5\" >0.5242</td>\n",
+       "      <td id=\"T_0f3db_row3_col6\" class=\"data row3 col6\" >0.4199</td>\n",
+       "      <td id=\"T_0f3db_row3_col7\" class=\"data row3 col7\" >0.2890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0f3db_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_0f3db_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Regressor</td>\n",
+       "      <td id=\"T_0f3db_row4_col1\" class=\"data row4 col1\" >27.4739</td>\n",
+       "      <td id=\"T_0f3db_row4_col2\" class=\"data row4 col2\" >1786.8737</td>\n",
+       "      <td id=\"T_0f3db_row4_col3\" class=\"data row4 col3\" >40.3415</td>\n",
+       "      <td id=\"T_0f3db_row4_col4\" class=\"data row4 col4\" >0.7225</td>\n",
+       "      <td id=\"T_0f3db_row4_col5\" class=\"data row4 col5\" >0.5139</td>\n",
+       "      <td id=\"T_0f3db_row4_col6\" class=\"data row4 col6\" >0.4668</td>\n",
+       "      <td id=\"T_0f3db_row4_col7\" class=\"data row4 col7\" >0.0270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0f3db_level0_row5\" class=\"row_heading level0 row5\" >3</th>\n",
+       "      <td id=\"T_0f3db_row5_col0\" class=\"data row5 col0\" >Linear Regression</td>\n",
+       "      <td id=\"T_0f3db_row5_col1\" class=\"data row5 col1\" >40.8649</td>\n",
+       "      <td id=\"T_0f3db_row5_col2\" class=\"data row5 col2\" >3029.9196</td>\n",
+       "      <td id=\"T_0f3db_row5_col3\" class=\"data row5 col3\" >53.8883</td>\n",
+       "      <td id=\"T_0f3db_row5_col4\" class=\"data row5 col4\" >0.5328</td>\n",
+       "      <td id=\"T_0f3db_row5_col5\" class=\"data row5 col5\" >0.6835</td>\n",
+       "      <td id=\"T_0f3db_row5_col6\" class=\"data row5 col6\" >0.6440</td>\n",
+       "      <td id=\"T_0f3db_row5_col7\" class=\"data row5 col7\" >0.2530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0f3db_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_0f3db_row6_col0\" class=\"data row6 col0\" >ExponentialRegressor</td>\n",
+       "      <td id=\"T_0f3db_row6_col1\" class=\"data row6 col1\" >37.4157</td>\n",
+       "      <td id=\"T_0f3db_row6_col2\" class=\"data row6 col2\" >3127.3444</td>\n",
+       "      <td id=\"T_0f3db_row6_col3\" class=\"data row6 col3\" >50.8761</td>\n",
+       "      <td id=\"T_0f3db_row6_col4\" class=\"data row6 col4\" >0.4162</td>\n",
+       "      <td id=\"T_0f3db_row6_col5\" class=\"data row6 col5\" >0.6428</td>\n",
+       "      <td id=\"T_0f3db_row6_col6\" class=\"data row6 col6\" >0.5885</td>\n",
+       "      <td id=\"T_0f3db_row6_col7\" class=\"data row6 col7\" >0.0240</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f834ed257b0>"
+       "<pandas.io.formats.style.Styler at 0x7fea5768d450>"
       ]
      },
      "metadata": {},
@@ -794,13 +723,15 @@
     }
    ],
    "source": [
+    "er = ExponentialRegressor()\n",
+    "\n",
     "# compare baseline models\n",
-    "best = exp.compare_models()\n"
+    "best = exp.compare_models(include = [\"rf\",\"ada\",\"dt\",\"lr\",\"gbr\",svr_rbf,er])\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "f8455018-e6b6-4e63-b697-4a2137016550",
    "metadata": {},
    "outputs": [
@@ -813,7 +744,7 @@
        "AdaBoostRegressor(random_state=123)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -824,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "9f6fec2e-c800-4c9d-afce-c1778d4b0ca1",
    "metadata": {},
    "outputs": [
@@ -840,7 +771,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x550 with 2 Axes>"
       ]
@@ -856,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "70e04206-e625-452f-a94e-1d6998a4d9d3",
    "metadata": {},
    "outputs": [
@@ -872,7 +803,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x550 with 1 Axes>"
       ]

From d2eef52b711b8fb5a2fb117ece16d3c236e43a57 Mon Sep 17 00:00:00 2001
From: khzadeh <m.khodadadzadeh@utwente.nl>
Date: Thu, 4 May 2023 14:51:26 +0200
Subject: [PATCH 3/5] ebm added to the list of models

---
 .../mk_model_comparision_pycaret.ipynb        | 579 ++++++++++++------
 1 file changed, 405 insertions(+), 174 deletions(-)

diff --git a/docs/notebooks/mk_model_comparision_pycaret.ipynb b/docs/notebooks/mk_model_comparision_pycaret.ipynb
index 82539b74..da0e11c3 100644
--- a/docs/notebooks/mk_model_comparision_pycaret.ipynb
+++ b/docs/notebooks/mk_model_comparision_pycaret.ipynb
@@ -26,6 +26,19 @@
    "id": "778938b4-845c-48ce-83b9-6e368413ec93",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading dataset:  npn_obs\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2015_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2016_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2017_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2018_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2019_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2020_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -38,13 +51,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading dataset:  npn_obs\n",
-      "/tmp/data/rnpn/rnpn_npn_data_y_2015_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
-      "/tmp/data/rnpn/rnpn_npn_data_y_2016_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
-      "/tmp/data/rnpn/rnpn_npn_data_y_2017_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
-      "/tmp/data/rnpn/rnpn_npn_data_y_2018_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
-      "/tmp/data/rnpn/rnpn_npn_data_y_2019_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
-      "/tmp/data/rnpn/rnpn_npn_data_y_2020_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
       "Downloading dataset:  daymet\n"
      ]
     },
@@ -250,15 +256,15 @@
        "</div>"
       ],
       "text/plain": [
-       "   year                       geometry  breaking leaf buds_doy  tmin_1  \\\n",
-       "0  2015  POINT (-122.357971 47.754948)                    50.0    3.81   \n",
+       "   year                       geometry  breaking leaf buds_doy  tmin_1   \n",
+       "0  2015  POINT (-122.357971 47.754948)                    50.0    3.81  \\\n",
        "1  2015  POINT (-122.377419 47.776241)                    50.0    3.68   \n",
        "2  2015  POINT (-122.185921 47.255966)                    56.0    3.27   \n",
        "3  2015  POINT (-121.861725 47.952686)                    50.0    1.93   \n",
        "4  2015  POINT (-122.686279 45.513168)                    99.0    3.70   \n",
        "\n",
-       "   tmin_2  tmin_3  tmin_4  tmin_5  tmin_6  tmin_7  ...  tmax_3  tmax_4  \\\n",
-       "0   5.000    5.59   4.640   10.19  11.625   14.50  ...   14.26  14.620   \n",
+       "   tmin_2  tmin_3  tmin_4  tmin_5  tmin_6  tmin_7  ...  tmax_3  tmax_4   \n",
+       "0   5.000    5.59   4.640   10.19  11.625   14.50  ...   14.26  14.620  \\\n",
        "1   4.955    5.56   4.585   10.11  11.540   14.41  ...   14.25  14.635   \n",
        "2   4.915    5.66   4.920    9.83  11.760   14.66  ...   14.04  14.070   \n",
        "3   3.440    4.21   2.920    9.24  10.015   12.76  ...   13.13  13.765   \n",
@@ -380,119 +386,119 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_8bc65_row8_col1 {\n",
+       "#T_c1435_row8_col1 {\n",
        "  background-color: lightgreen;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_8bc65\">\n",
+       "<table id=\"T_c1435\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_8bc65_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
-       "      <th id=\"T_8bc65_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
+       "      <th id=\"T_c1435_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
+       "      <th id=\"T_c1435_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_8bc65_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
-       "      <td id=\"T_8bc65_row0_col1\" class=\"data row0 col1\" >123</td>\n",
+       "      <th id=\"T_c1435_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_c1435_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
+       "      <td id=\"T_c1435_row0_col1\" class=\"data row0 col1\" >123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_8bc65_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
-       "      <td id=\"T_8bc65_row1_col1\" class=\"data row1 col1\" >breaking leaf buds_doy</td>\n",
+       "      <th id=\"T_c1435_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_c1435_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
+       "      <td id=\"T_c1435_row1_col1\" class=\"data row1 col1\" >breaking leaf buds_doy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_8bc65_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
-       "      <td id=\"T_8bc65_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
+       "      <th id=\"T_c1435_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_c1435_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
+       "      <td id=\"T_c1435_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_8bc65_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
-       "      <td id=\"T_8bc65_row3_col1\" class=\"data row3 col1\" >(241, 25)</td>\n",
+       "      <th id=\"T_c1435_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_c1435_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
+       "      <td id=\"T_c1435_row3_col1\" class=\"data row3 col1\" >(241, 25)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_8bc65_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
-       "      <td id=\"T_8bc65_row4_col1\" class=\"data row4 col1\" >(241, 25)</td>\n",
+       "      <th id=\"T_c1435_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_c1435_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
+       "      <td id=\"T_c1435_row4_col1\" class=\"data row4 col1\" >(241, 25)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_8bc65_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
-       "      <td id=\"T_8bc65_row5_col1\" class=\"data row5 col1\" >(168, 25)</td>\n",
+       "      <th id=\"T_c1435_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_c1435_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
+       "      <td id=\"T_c1435_row5_col1\" class=\"data row5 col1\" >(168, 25)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_8bc65_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
-       "      <td id=\"T_8bc65_row6_col1\" class=\"data row6 col1\" >(73, 25)</td>\n",
+       "      <th id=\"T_c1435_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_c1435_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
+       "      <td id=\"T_c1435_row6_col1\" class=\"data row6 col1\" >(73, 25)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_8bc65_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
-       "      <td id=\"T_8bc65_row7_col1\" class=\"data row7 col1\" >24</td>\n",
+       "      <th id=\"T_c1435_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_c1435_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
+       "      <td id=\"T_c1435_row7_col1\" class=\"data row7 col1\" >24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_8bc65_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
-       "      <td id=\"T_8bc65_row8_col1\" class=\"data row8 col1\" >True</td>\n",
+       "      <th id=\"T_c1435_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_c1435_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
+       "      <td id=\"T_c1435_row8_col1\" class=\"data row8 col1\" >True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_8bc65_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
-       "      <td id=\"T_8bc65_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
+       "      <th id=\"T_c1435_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_c1435_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
+       "      <td id=\"T_c1435_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "      <td id=\"T_8bc65_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
-       "      <td id=\"T_8bc65_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
+       "      <th id=\"T_c1435_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_c1435_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
+       "      <td id=\"T_c1435_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "      <td id=\"T_8bc65_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
-       "      <td id=\"T_8bc65_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
+       "      <th id=\"T_c1435_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_c1435_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
+       "      <td id=\"T_c1435_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "      <td id=\"T_8bc65_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
-       "      <td id=\"T_8bc65_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
+       "      <th id=\"T_c1435_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_c1435_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
+       "      <td id=\"T_c1435_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "      <td id=\"T_8bc65_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
-       "      <td id=\"T_8bc65_row13_col1\" class=\"data row13 col1\" >10</td>\n",
+       "      <th id=\"T_c1435_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_c1435_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
+       "      <td id=\"T_c1435_row13_col1\" class=\"data row13 col1\" >10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
-       "      <td id=\"T_8bc65_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
-       "      <td id=\"T_8bc65_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
+       "      <th id=\"T_c1435_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_c1435_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
+       "      <td id=\"T_c1435_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
-       "      <td id=\"T_8bc65_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
-       "      <td id=\"T_8bc65_row15_col1\" class=\"data row15 col1\" >False</td>\n",
+       "      <th id=\"T_c1435_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_c1435_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
+       "      <td id=\"T_c1435_row15_col1\" class=\"data row15 col1\" >False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
-       "      <td id=\"T_8bc65_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
-       "      <td id=\"T_8bc65_row16_col1\" class=\"data row16 col1\" >False</td>\n",
+       "      <th id=\"T_c1435_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+       "      <td id=\"T_c1435_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
+       "      <td id=\"T_c1435_row16_col1\" class=\"data row16 col1\" >False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
-       "      <td id=\"T_8bc65_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
-       "      <td id=\"T_8bc65_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
+       "      <th id=\"T_c1435_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+       "      <td id=\"T_c1435_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
+       "      <td id=\"T_c1435_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8bc65_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
-       "      <td id=\"T_8bc65_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
-       "      <td id=\"T_8bc65_row18_col1\" class=\"data row18 col1\" >be5f</td>\n",
+       "      <th id=\"T_c1435_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+       "      <td id=\"T_c1435_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
+       "      <td id=\"T_c1435_row18_col1\" class=\"data row18 col1\" >5e4e</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fea576cba00>"
+       "<pandas.io.formats.style.Styler at 0x7f3244980580>"
       ]
      },
      "metadata": {},
@@ -501,7 +507,7 @@
     {
      "data": {
       "text/plain": [
-       "<pycaret.regression.oop.RegressionExperiment at 0x7fea62539cf0>"
+       "<pycaret.regression.oop.RegressionExperiment at 0x7f324ba4fa90>"
       ]
      },
      "execution_count": 11,
@@ -572,6 +578,222 @@
   {
    "cell_type": "code",
    "execution_count": 14,
+   "id": "23e89931-7e3b-43a2-aec5-d0c5c9d709dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "er = ExponentialRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8fb22277-eb78-4472-98ea-19a85e13da30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_4dca1_row10_col0, #T_4dca1_row10_col1, #T_4dca1_row10_col2, #T_4dca1_row10_col3, #T_4dca1_row10_col4, #T_4dca1_row10_col5 {\n",
+       "  background: yellow;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_4dca1\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_4dca1_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
+       "      <th id=\"T_4dca1_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
+       "      <th id=\"T_4dca1_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
+       "      <th id=\"T_4dca1_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
+       "      <th id=\"T_4dca1_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
+       "      <th id=\"T_4dca1_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Fold</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_4dca1_row0_col0\" class=\"data row0 col0\" >33.4232</td>\n",
+       "      <td id=\"T_4dca1_row0_col1\" class=\"data row0 col1\" >2443.4244</td>\n",
+       "      <td id=\"T_4dca1_row0_col2\" class=\"data row0 col2\" >49.4310</td>\n",
+       "      <td id=\"T_4dca1_row0_col3\" class=\"data row0 col3\" >0.5143</td>\n",
+       "      <td id=\"T_4dca1_row0_col4\" class=\"data row0 col4\" >0.6364</td>\n",
+       "      <td id=\"T_4dca1_row0_col5\" class=\"data row0 col5\" >0.5511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_4dca1_row1_col0\" class=\"data row1 col0\" >45.4228</td>\n",
+       "      <td id=\"T_4dca1_row1_col1\" class=\"data row1 col1\" >5940.9017</td>\n",
+       "      <td id=\"T_4dca1_row1_col2\" class=\"data row1 col2\" >77.0772</td>\n",
+       "      <td id=\"T_4dca1_row1_col3\" class=\"data row1 col3\" >0.4499</td>\n",
+       "      <td id=\"T_4dca1_row1_col4\" class=\"data row1 col4\" >0.7165</td>\n",
+       "      <td id=\"T_4dca1_row1_col5\" class=\"data row1 col5\" >0.4608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_4dca1_row2_col0\" class=\"data row2 col0\" >23.5023</td>\n",
+       "      <td id=\"T_4dca1_row2_col1\" class=\"data row2 col1\" >1240.1786</td>\n",
+       "      <td id=\"T_4dca1_row2_col2\" class=\"data row2 col2\" >35.2162</td>\n",
+       "      <td id=\"T_4dca1_row2_col3\" class=\"data row2 col3\" >0.8606</td>\n",
+       "      <td id=\"T_4dca1_row2_col4\" class=\"data row2 col4\" >0.3306</td>\n",
+       "      <td id=\"T_4dca1_row2_col5\" class=\"data row2 col5\" >0.2950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_4dca1_row3_col0\" class=\"data row3 col0\" >34.4129</td>\n",
+       "      <td id=\"T_4dca1_row3_col1\" class=\"data row3 col1\" >2149.9453</td>\n",
+       "      <td id=\"T_4dca1_row3_col2\" class=\"data row3 col2\" >46.3675</td>\n",
+       "      <td id=\"T_4dca1_row3_col3\" class=\"data row3 col3\" >0.4625</td>\n",
+       "      <td id=\"T_4dca1_row3_col4\" class=\"data row3 col4\" >0.6190</td>\n",
+       "      <td id=\"T_4dca1_row3_col5\" class=\"data row3 col5\" >0.7070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_4dca1_row4_col0\" class=\"data row4 col0\" >27.7259</td>\n",
+       "      <td id=\"T_4dca1_row4_col1\" class=\"data row4 col1\" >1323.0719</td>\n",
+       "      <td id=\"T_4dca1_row4_col2\" class=\"data row4 col2\" >36.3741</td>\n",
+       "      <td id=\"T_4dca1_row4_col3\" class=\"data row4 col3\" >0.7576</td>\n",
+       "      <td id=\"T_4dca1_row4_col4\" class=\"data row4 col4\" >0.5151</td>\n",
+       "      <td id=\"T_4dca1_row4_col5\" class=\"data row4 col5\" >0.4690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_4dca1_row5_col0\" class=\"data row5 col0\" >23.5232</td>\n",
+       "      <td id=\"T_4dca1_row5_col1\" class=\"data row5 col1\" >1046.4490</td>\n",
+       "      <td id=\"T_4dca1_row5_col2\" class=\"data row5 col2\" >32.3489</td>\n",
+       "      <td id=\"T_4dca1_row5_col3\" class=\"data row5 col3\" >0.8327</td>\n",
+       "      <td id=\"T_4dca1_row5_col4\" class=\"data row5 col4\" >0.4810</td>\n",
+       "      <td id=\"T_4dca1_row5_col5\" class=\"data row5 col5\" >0.5022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_4dca1_row6_col0\" class=\"data row6 col0\" >21.7696</td>\n",
+       "      <td id=\"T_4dca1_row6_col1\" class=\"data row6 col1\" >642.4940</td>\n",
+       "      <td id=\"T_4dca1_row6_col2\" class=\"data row6 col2\" >25.3475</td>\n",
+       "      <td id=\"T_4dca1_row6_col3\" class=\"data row6 col3\" >0.8318</td>\n",
+       "      <td id=\"T_4dca1_row6_col4\" class=\"data row6 col4\" >0.3076</td>\n",
+       "      <td id=\"T_4dca1_row6_col5\" class=\"data row6 col5\" >0.2769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_4dca1_row7_col0\" class=\"data row7 col0\" >47.4773</td>\n",
+       "      <td id=\"T_4dca1_row7_col1\" class=\"data row7 col1\" >4386.5614</td>\n",
+       "      <td id=\"T_4dca1_row7_col2\" class=\"data row7 col2\" >66.2311</td>\n",
+       "      <td id=\"T_4dca1_row7_col3\" class=\"data row7 col3\" >0.4914</td>\n",
+       "      <td id=\"T_4dca1_row7_col4\" class=\"data row7 col4\" >0.7356</td>\n",
+       "      <td id=\"T_4dca1_row7_col5\" class=\"data row7 col5\" >0.8810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_4dca1_row8_col0\" class=\"data row8 col0\" >29.2698</td>\n",
+       "      <td id=\"T_4dca1_row8_col1\" class=\"data row8 col1\" >1650.6301</td>\n",
+       "      <td id=\"T_4dca1_row8_col2\" class=\"data row8 col2\" >40.6279</td>\n",
+       "      <td id=\"T_4dca1_row8_col3\" class=\"data row8 col3\" >0.7769</td>\n",
+       "      <td id=\"T_4dca1_row8_col4\" class=\"data row8 col4\" >0.4712</td>\n",
+       "      <td id=\"T_4dca1_row8_col5\" class=\"data row8 col5\" >0.4820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_4dca1_row9_col0\" class=\"data row9 col0\" >29.0206</td>\n",
+       "      <td id=\"T_4dca1_row9_col1\" class=\"data row9 col1\" >1338.4585</td>\n",
+       "      <td id=\"T_4dca1_row9_col2\" class=\"data row9 col2\" >36.5849</td>\n",
+       "      <td id=\"T_4dca1_row9_col3\" class=\"data row9 col3\" >0.8856</td>\n",
+       "      <td id=\"T_4dca1_row9_col4\" class=\"data row9 col4\" >0.4496</td>\n",
+       "      <td id=\"T_4dca1_row9_col5\" class=\"data row9 col5\" >0.4591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
+       "      <td id=\"T_4dca1_row10_col0\" class=\"data row10 col0\" >31.5548</td>\n",
+       "      <td id=\"T_4dca1_row10_col1\" class=\"data row10 col1\" >2216.2115</td>\n",
+       "      <td id=\"T_4dca1_row10_col2\" class=\"data row10 col2\" >44.5606</td>\n",
+       "      <td id=\"T_4dca1_row10_col3\" class=\"data row10 col3\" >0.6863</td>\n",
+       "      <td id=\"T_4dca1_row10_col4\" class=\"data row10 col4\" >0.5263</td>\n",
+       "      <td id=\"T_4dca1_row10_col5\" class=\"data row10 col5\" >0.5084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4dca1_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
+       "      <td id=\"T_4dca1_row11_col0\" class=\"data row11 col0\" >8.4197</td>\n",
+       "      <td id=\"T_4dca1_row11_col1\" class=\"data row11 col1\" >1591.3942</td>\n",
+       "      <td id=\"T_4dca1_row11_col2\" class=\"data row11 col2\" >15.1843</td>\n",
+       "      <td id=\"T_4dca1_row11_col3\" class=\"data row11 col3\" >0.1731</td>\n",
+       "      <td id=\"T_4dca1_row11_col4\" class=\"data row11 col4\" >0.1407</td>\n",
+       "      <td id=\"T_4dca1_row11_col5\" class=\"data row11 col5\" >0.1692</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f32451a4130>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExplainableBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExplainableBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>ExplainableBoostingRegressor()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "ExplainableBoostingRegressor()"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  RegressionTree, LinearRegression, LogisticRegression not implemented inline with the pycaret\n",
+    "from interpret.glassbox import ExplainableBoostingRegressor\n",
+    "model = ExplainableBoostingRegressor()\n",
+    "exp.create_model(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "42c786bb-c2c7-404b-97c2-1352532945a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from interpret.glassbox import ExplainableBoostingRegressor\n",
+    "ebm = ExplainableBoostingRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "id": "edc800e5-5884-4290-9034-38d25aeb75ea",
    "metadata": {},
    "outputs": [
@@ -589,123 +811,134 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_0f3db th {\n",
+       "#T_e1944 th {\n",
        "  text-align: left;\n",
        "}\n",
-       "#T_0f3db_row0_col0, #T_0f3db_row0_col1, #T_0f3db_row0_col2, #T_0f3db_row0_col3, #T_0f3db_row0_col5, #T_0f3db_row0_col6, #T_0f3db_row1_col0, #T_0f3db_row1_col4, #T_0f3db_row1_col6, #T_0f3db_row2_col0, #T_0f3db_row2_col1, #T_0f3db_row2_col2, #T_0f3db_row2_col3, #T_0f3db_row2_col4, #T_0f3db_row2_col5, #T_0f3db_row2_col6, #T_0f3db_row3_col0, #T_0f3db_row3_col1, #T_0f3db_row3_col2, #T_0f3db_row3_col3, #T_0f3db_row3_col4, #T_0f3db_row3_col5, #T_0f3db_row4_col0, #T_0f3db_row4_col1, #T_0f3db_row4_col2, #T_0f3db_row4_col3, #T_0f3db_row4_col4, #T_0f3db_row4_col5, #T_0f3db_row4_col6, #T_0f3db_row5_col0, #T_0f3db_row5_col1, #T_0f3db_row5_col2, #T_0f3db_row5_col3, #T_0f3db_row5_col4, #T_0f3db_row5_col5, #T_0f3db_row5_col6, #T_0f3db_row6_col0, #T_0f3db_row6_col1, #T_0f3db_row6_col2, #T_0f3db_row6_col3, #T_0f3db_row6_col4, #T_0f3db_row6_col5, #T_0f3db_row6_col6 {\n",
+       "#T_e1944_row0_col0, #T_e1944_row0_col1, #T_e1944_row0_col2, #T_e1944_row0_col3, #T_e1944_row0_col5, #T_e1944_row0_col6, #T_e1944_row1_col0, #T_e1944_row1_col4, #T_e1944_row1_col6, #T_e1944_row2_col0, #T_e1944_row2_col1, #T_e1944_row2_col2, #T_e1944_row2_col3, #T_e1944_row2_col4, #T_e1944_row2_col5, #T_e1944_row2_col6, #T_e1944_row3_col0, #T_e1944_row3_col1, #T_e1944_row3_col2, #T_e1944_row3_col3, #T_e1944_row3_col4, #T_e1944_row3_col5, #T_e1944_row4_col0, #T_e1944_row4_col1, #T_e1944_row4_col2, #T_e1944_row4_col3, #T_e1944_row4_col4, #T_e1944_row4_col5, #T_e1944_row4_col6, #T_e1944_row5_col0, #T_e1944_row5_col1, #T_e1944_row5_col2, #T_e1944_row5_col3, #T_e1944_row5_col4, #T_e1944_row5_col5, #T_e1944_row5_col6, #T_e1944_row6_col0, #T_e1944_row6_col1, #T_e1944_row6_col2, #T_e1944_row6_col3, #T_e1944_row6_col4, #T_e1944_row6_col5, #T_e1944_row6_col6, #T_e1944_row7_col0, #T_e1944_row7_col1, #T_e1944_row7_col2, #T_e1944_row7_col3, #T_e1944_row7_col4, #T_e1944_row7_col5, #T_e1944_row7_col6 {\n",
        "  text-align: left;\n",
        "}\n",
-       "#T_0f3db_row0_col4, #T_0f3db_row1_col1, #T_0f3db_row1_col2, #T_0f3db_row1_col3, #T_0f3db_row1_col5, #T_0f3db_row3_col6 {\n",
+       "#T_e1944_row0_col4, #T_e1944_row1_col1, #T_e1944_row1_col2, #T_e1944_row1_col3, #T_e1944_row1_col5, #T_e1944_row3_col6 {\n",
        "  text-align: left;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_0f3db_row0_col7, #T_0f3db_row1_col7, #T_0f3db_row3_col7, #T_0f3db_row4_col7, #T_0f3db_row5_col7, #T_0f3db_row6_col7 {\n",
+       "#T_e1944_row0_col7, #T_e1944_row1_col7, #T_e1944_row2_col7, #T_e1944_row3_col7, #T_e1944_row4_col7, #T_e1944_row5_col7, #T_e1944_row7_col7 {\n",
        "  text-align: left;\n",
        "  background-color: lightgrey;\n",
        "}\n",
-       "#T_0f3db_row2_col7 {\n",
+       "#T_e1944_row6_col7 {\n",
        "  text-align: left;\n",
        "  background-color: yellow;\n",
        "  background-color: lightgrey;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_0f3db\">\n",
+       "<table id=\"T_e1944\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_0f3db_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
-       "      <th id=\"T_0f3db_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
-       "      <th id=\"T_0f3db_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
-       "      <th id=\"T_0f3db_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
-       "      <th id=\"T_0f3db_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
-       "      <th id=\"T_0f3db_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
-       "      <th id=\"T_0f3db_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
-       "      <th id=\"T_0f3db_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
+       "      <th id=\"T_e1944_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
+       "      <th id=\"T_e1944_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
+       "      <th id=\"T_e1944_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
+       "      <th id=\"T_e1944_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
+       "      <th id=\"T_e1944_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
+       "      <th id=\"T_e1944_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
+       "      <th id=\"T_e1944_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
+       "      <th id=\"T_e1944_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_0f3db_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_0f3db_row0_col0\" class=\"data row0 col0\" >AdaBoost Regressor</td>\n",
-       "      <td id=\"T_0f3db_row0_col1\" class=\"data row0 col1\" >26.6921</td>\n",
-       "      <td id=\"T_0f3db_row0_col2\" class=\"data row0 col2\" >1470.7824</td>\n",
-       "      <td id=\"T_0f3db_row0_col3\" class=\"data row0 col3\" >36.1062</td>\n",
-       "      <td id=\"T_0f3db_row0_col4\" class=\"data row0 col4\" >0.7910</td>\n",
-       "      <td id=\"T_0f3db_row0_col5\" class=\"data row0 col5\" >0.4942</td>\n",
-       "      <td id=\"T_0f3db_row0_col6\" class=\"data row0 col6\" >0.4973</td>\n",
-       "      <td id=\"T_0f3db_row0_col7\" class=\"data row0 col7\" >0.3000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0f3db_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
-       "      <td id=\"T_0f3db_row1_col0\" class=\"data row1 col0\" >Random Forest Regressor</td>\n",
-       "      <td id=\"T_0f3db_row1_col1\" class=\"data row1 col1\" >24.5937</td>\n",
-       "      <td id=\"T_0f3db_row1_col2\" class=\"data row1 col2\" >1467.8504</td>\n",
-       "      <td id=\"T_0f3db_row1_col3\" class=\"data row1 col3\" >35.9053</td>\n",
-       "      <td id=\"T_0f3db_row1_col4\" class=\"data row1 col4\" >0.7857</td>\n",
-       "      <td id=\"T_0f3db_row1_col5\" class=\"data row1 col5\" >0.4724</td>\n",
-       "      <td id=\"T_0f3db_row1_col6\" class=\"data row1 col6\" >0.4236</td>\n",
-       "      <td id=\"T_0f3db_row1_col7\" class=\"data row1 col7\" >0.3810</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0f3db_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
-       "      <td id=\"T_0f3db_row2_col0\" class=\"data row2 col0\" >Support Vector Regression</td>\n",
-       "      <td id=\"T_0f3db_row2_col1\" class=\"data row2 col1\" >26.9678</td>\n",
-       "      <td id=\"T_0f3db_row2_col2\" class=\"data row2 col2\" >1684.4732</td>\n",
-       "      <td id=\"T_0f3db_row2_col3\" class=\"data row2 col3\" >39.4120</td>\n",
-       "      <td id=\"T_0f3db_row2_col4\" class=\"data row2 col4\" >0.7467</td>\n",
-       "      <td id=\"T_0f3db_row2_col5\" class=\"data row2 col5\" >0.5089</td>\n",
-       "      <td id=\"T_0f3db_row2_col6\" class=\"data row2 col6\" >0.4589</td>\n",
-       "      <td id=\"T_0f3db_row2_col7\" class=\"data row2 col7\" >0.0220</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0f3db_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
-       "      <td id=\"T_0f3db_row3_col0\" class=\"data row3 col0\" >Decision Tree Regressor</td>\n",
-       "      <td id=\"T_0f3db_row3_col1\" class=\"data row3 col1\" >26.0720</td>\n",
-       "      <td id=\"T_0f3db_row3_col2\" class=\"data row3 col2\" >1761.6493</td>\n",
-       "      <td id=\"T_0f3db_row3_col3\" class=\"data row3 col3\" >39.8640</td>\n",
-       "      <td id=\"T_0f3db_row3_col4\" class=\"data row3 col4\" >0.7286</td>\n",
-       "      <td id=\"T_0f3db_row3_col5\" class=\"data row3 col5\" >0.5242</td>\n",
-       "      <td id=\"T_0f3db_row3_col6\" class=\"data row3 col6\" >0.4199</td>\n",
-       "      <td id=\"T_0f3db_row3_col7\" class=\"data row3 col7\" >0.2890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0f3db_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_0f3db_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Regressor</td>\n",
-       "      <td id=\"T_0f3db_row4_col1\" class=\"data row4 col1\" >27.4739</td>\n",
-       "      <td id=\"T_0f3db_row4_col2\" class=\"data row4 col2\" >1786.8737</td>\n",
-       "      <td id=\"T_0f3db_row4_col3\" class=\"data row4 col3\" >40.3415</td>\n",
-       "      <td id=\"T_0f3db_row4_col4\" class=\"data row4 col4\" >0.7225</td>\n",
-       "      <td id=\"T_0f3db_row4_col5\" class=\"data row4 col5\" >0.5139</td>\n",
-       "      <td id=\"T_0f3db_row4_col6\" class=\"data row4 col6\" >0.4668</td>\n",
-       "      <td id=\"T_0f3db_row4_col7\" class=\"data row4 col7\" >0.0270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0f3db_level0_row5\" class=\"row_heading level0 row5\" >3</th>\n",
-       "      <td id=\"T_0f3db_row5_col0\" class=\"data row5 col0\" >Linear Regression</td>\n",
-       "      <td id=\"T_0f3db_row5_col1\" class=\"data row5 col1\" >40.8649</td>\n",
-       "      <td id=\"T_0f3db_row5_col2\" class=\"data row5 col2\" >3029.9196</td>\n",
-       "      <td id=\"T_0f3db_row5_col3\" class=\"data row5 col3\" >53.8883</td>\n",
-       "      <td id=\"T_0f3db_row5_col4\" class=\"data row5 col4\" >0.5328</td>\n",
-       "      <td id=\"T_0f3db_row5_col5\" class=\"data row5 col5\" >0.6835</td>\n",
-       "      <td id=\"T_0f3db_row5_col6\" class=\"data row5 col6\" >0.6440</td>\n",
-       "      <td id=\"T_0f3db_row5_col7\" class=\"data row5 col7\" >0.2530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_0f3db_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_0f3db_row6_col0\" class=\"data row6 col0\" >ExponentialRegressor</td>\n",
-       "      <td id=\"T_0f3db_row6_col1\" class=\"data row6 col1\" >37.4157</td>\n",
-       "      <td id=\"T_0f3db_row6_col2\" class=\"data row6 col2\" >3127.3444</td>\n",
-       "      <td id=\"T_0f3db_row6_col3\" class=\"data row6 col3\" >50.8761</td>\n",
-       "      <td id=\"T_0f3db_row6_col4\" class=\"data row6 col4\" >0.4162</td>\n",
-       "      <td id=\"T_0f3db_row6_col5\" class=\"data row6 col5\" >0.6428</td>\n",
-       "      <td id=\"T_0f3db_row6_col6\" class=\"data row6 col6\" >0.5885</td>\n",
-       "      <td id=\"T_0f3db_row6_col7\" class=\"data row6 col7\" >0.0240</td>\n",
+       "      <th id=\"T_e1944_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_e1944_row0_col0\" class=\"data row0 col0\" >AdaBoost Regressor</td>\n",
+       "      <td id=\"T_e1944_row0_col1\" class=\"data row0 col1\" >26.6921</td>\n",
+       "      <td id=\"T_e1944_row0_col2\" class=\"data row0 col2\" >1470.7824</td>\n",
+       "      <td id=\"T_e1944_row0_col3\" class=\"data row0 col3\" >36.1062</td>\n",
+       "      <td id=\"T_e1944_row0_col4\" class=\"data row0 col4\" >0.7910</td>\n",
+       "      <td id=\"T_e1944_row0_col5\" class=\"data row0 col5\" >0.4942</td>\n",
+       "      <td id=\"T_e1944_row0_col6\" class=\"data row0 col6\" >0.4973</td>\n",
+       "      <td id=\"T_e1944_row0_col7\" class=\"data row0 col7\" >0.2710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e1944_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
+       "      <td id=\"T_e1944_row1_col0\" class=\"data row1 col0\" >Random Forest Regressor</td>\n",
+       "      <td id=\"T_e1944_row1_col1\" class=\"data row1 col1\" >24.5937</td>\n",
+       "      <td id=\"T_e1944_row1_col2\" class=\"data row1 col2\" >1467.8504</td>\n",
+       "      <td id=\"T_e1944_row1_col3\" class=\"data row1 col3\" >35.9053</td>\n",
+       "      <td id=\"T_e1944_row1_col4\" class=\"data row1 col4\" >0.7857</td>\n",
+       "      <td id=\"T_e1944_row1_col5\" class=\"data row1 col5\" >0.4724</td>\n",
+       "      <td id=\"T_e1944_row1_col6\" class=\"data row1 col6\" >0.4236</td>\n",
+       "      <td id=\"T_e1944_row1_col7\" class=\"data row1 col7\" >0.2740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e1944_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
+       "      <td id=\"T_e1944_row2_col0\" class=\"data row2 col0\" >Support Vector Regression</td>\n",
+       "      <td id=\"T_e1944_row2_col1\" class=\"data row2 col1\" >26.9678</td>\n",
+       "      <td id=\"T_e1944_row2_col2\" class=\"data row2 col2\" >1684.4732</td>\n",
+       "      <td id=\"T_e1944_row2_col3\" class=\"data row2 col3\" >39.4120</td>\n",
+       "      <td id=\"T_e1944_row2_col4\" class=\"data row2 col4\" >0.7467</td>\n",
+       "      <td id=\"T_e1944_row2_col5\" class=\"data row2 col5\" >0.5089</td>\n",
+       "      <td id=\"T_e1944_row2_col6\" class=\"data row2 col6\" >0.4589</td>\n",
+       "      <td id=\"T_e1944_row2_col7\" class=\"data row2 col7\" >0.0240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e1944_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
+       "      <td id=\"T_e1944_row3_col0\" class=\"data row3 col0\" >Decision Tree Regressor</td>\n",
+       "      <td id=\"T_e1944_row3_col1\" class=\"data row3 col1\" >26.0720</td>\n",
+       "      <td id=\"T_e1944_row3_col2\" class=\"data row3 col2\" >1761.6493</td>\n",
+       "      <td id=\"T_e1944_row3_col3\" class=\"data row3 col3\" >39.8640</td>\n",
+       "      <td id=\"T_e1944_row3_col4\" class=\"data row3 col4\" >0.7286</td>\n",
+       "      <td id=\"T_e1944_row3_col5\" class=\"data row3 col5\" >0.5242</td>\n",
+       "      <td id=\"T_e1944_row3_col6\" class=\"data row3 col6\" >0.4199</td>\n",
+       "      <td id=\"T_e1944_row3_col7\" class=\"data row3 col7\" >0.2000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e1944_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_e1944_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Regressor</td>\n",
+       "      <td id=\"T_e1944_row4_col1\" class=\"data row4 col1\" >27.4739</td>\n",
+       "      <td id=\"T_e1944_row4_col2\" class=\"data row4 col2\" >1786.8737</td>\n",
+       "      <td id=\"T_e1944_row4_col3\" class=\"data row4 col3\" >40.3415</td>\n",
+       "      <td id=\"T_e1944_row4_col4\" class=\"data row4 col4\" >0.7225</td>\n",
+       "      <td id=\"T_e1944_row4_col5\" class=\"data row4 col5\" >0.5139</td>\n",
+       "      <td id=\"T_e1944_row4_col6\" class=\"data row4 col6\" >0.4668</td>\n",
+       "      <td id=\"T_e1944_row4_col7\" class=\"data row4 col7\" >0.0320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e1944_level0_row5\" class=\"row_heading level0 row5\" >7</th>\n",
+       "      <td id=\"T_e1944_row5_col0\" class=\"data row5 col0\" >ExplainableBoostingRegressor</td>\n",
+       "      <td id=\"T_e1944_row5_col1\" class=\"data row5 col1\" >31.5548</td>\n",
+       "      <td id=\"T_e1944_row5_col2\" class=\"data row5 col2\" >2216.2115</td>\n",
+       "      <td id=\"T_e1944_row5_col3\" class=\"data row5 col3\" >44.5606</td>\n",
+       "      <td id=\"T_e1944_row5_col4\" class=\"data row5 col4\" >0.6863</td>\n",
+       "      <td id=\"T_e1944_row5_col5\" class=\"data row5 col5\" >0.5263</td>\n",
+       "      <td id=\"T_e1944_row5_col6\" class=\"data row5 col6\" >0.5084</td>\n",
+       "      <td id=\"T_e1944_row5_col7\" class=\"data row5 col7\" >0.0310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e1944_level0_row6\" class=\"row_heading level0 row6\" >3</th>\n",
+       "      <td id=\"T_e1944_row6_col0\" class=\"data row6 col0\" >Linear Regression</td>\n",
+       "      <td id=\"T_e1944_row6_col1\" class=\"data row6 col1\" >40.8649</td>\n",
+       "      <td id=\"T_e1944_row6_col2\" class=\"data row6 col2\" >3029.9195</td>\n",
+       "      <td id=\"T_e1944_row6_col3\" class=\"data row6 col3\" >53.8883</td>\n",
+       "      <td id=\"T_e1944_row6_col4\" class=\"data row6 col4\" >0.5328</td>\n",
+       "      <td id=\"T_e1944_row6_col5\" class=\"data row6 col5\" >0.6835</td>\n",
+       "      <td id=\"T_e1944_row6_col6\" class=\"data row6 col6\" >0.6440</td>\n",
+       "      <td id=\"T_e1944_row6_col7\" class=\"data row6 col7\" >0.0220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e1944_level0_row7\" class=\"row_heading level0 row7\" >6</th>\n",
+       "      <td id=\"T_e1944_row7_col0\" class=\"data row7 col0\" >ExponentialRegressor</td>\n",
+       "      <td id=\"T_e1944_row7_col1\" class=\"data row7 col1\" >37.4157</td>\n",
+       "      <td id=\"T_e1944_row7_col2\" class=\"data row7 col2\" >3127.3444</td>\n",
+       "      <td id=\"T_e1944_row7_col3\" class=\"data row7 col3\" >50.8761</td>\n",
+       "      <td id=\"T_e1944_row7_col4\" class=\"data row7 col4\" >0.4162</td>\n",
+       "      <td id=\"T_e1944_row7_col5\" class=\"data row7 col5\" >0.6428</td>\n",
+       "      <td id=\"T_e1944_row7_col6\" class=\"data row7 col6\" >0.5885</td>\n",
+       "      <td id=\"T_e1944_row7_col7\" class=\"data row7 col7\" >0.0270</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fea5768d450>"
+       "<pandas.io.formats.style.Styler at 0x7f324420f7f0>"
       ]
      },
      "metadata": {},
@@ -723,28 +956,26 @@
     }
    ],
    "source": [
-    "er = ExponentialRegressor()\n",
-    "\n",
     "# compare baseline models\n",
-    "best = exp.compare_models(include = [\"rf\",\"ada\",\"dt\",\"lr\",\"gbr\",svr_rbf,er])\n"
+    "best = exp.compare_models(include = [\"rf\",\"ada\",\"dt\",\"lr\",\"gbr\",svr_rbf,er,ebm])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "f8455018-e6b6-4e63-b697-4a2137016550",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor(random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor(random_state=123)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor(random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor(random_state=123)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "AdaBoostRegressor(random_state=123)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -755,7 +986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "9f6fec2e-c800-4c9d-afce-c1778d4b0ca1",
    "metadata": {},
    "outputs": [
@@ -771,7 +1002,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x550 with 2 Axes>"
       ]
@@ -787,7 +1018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "id": "70e04206-e625-452f-a94e-1d6998a4d9d3",
    "metadata": {},
    "outputs": [
@@ -803,7 +1034,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x550 with 1 Axes>"
       ]

From 167d47bfe50c0006c6a4d9dbaa42294b12cea4b4 Mon Sep 17 00:00:00 2001
From: khzadeh <m.khodadadzadeh@utwente.nl>
Date: Thu, 4 May 2023 14:55:19 +0200
Subject: [PATCH 4/5] comparing mrf with ebm and sklearn models and integration
 of these models in merf

---
 .../mk_recipe_rf_merf_ebm_integration.ipynb   | 1385 +++++++++++++++++
 1 file changed, 1385 insertions(+)
 create mode 100644 docs/notebooks/mk_recipe_rf_merf_ebm_integration.ipynb

diff --git a/docs/notebooks/mk_recipe_rf_merf_ebm_integration.ipynb b/docs/notebooks/mk_recipe_rf_merf_ebm_integration.ipynb
new file mode 100644
index 00000000..8f45fef8
--- /dev/null
+++ b/docs/notebooks/mk_recipe_rf_merf_ebm_integration.ipynb
@@ -0,0 +1,1385 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7ca85fd6-27fd-422f-833e-e6b4bb02499a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b69a1f5e-9708-4a35-ae76-f62f69a56312",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from springtime.main import Workflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "72a3ad90-6323-4ef9-8020-d6cf3cf4e60c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recipe = \"/home/jovyan/springtime/src/springtime/recipes/model_comparison_usecase.yaml\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc630bb0-ea34-46eb-80be-fbcefa397645",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading dataset:  npn_obs\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2015_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2016_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2017_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2018_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2019_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n",
+      "/tmp/data/rnpn/rnpn_npn_data_y_2020_Deciduous broadleaf_breaking leaf buds_Washington.csv already exists, skipping\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Dataset npn_obs loaded with 241 rows\n",
+      "Dataset npn_obs resampled to 241 rows\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading dataset:  daymet\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Dataset daymet loaded with 326310 rows\n",
+      "Dataset daymet resampled to 894 rows\n",
+      "Datesets joined to shape: (894, 25)\n",
+      "Data saved to: /tmp/output/data.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "Workflow.from_recipe(recipe).execute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1161a129-0ec3-4e5e-8142-5bf114f960c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>breaking leaf buds_doy</th>\n",
+       "      <th>tmin_1</th>\n",
+       "      <th>tmin_2</th>\n",
+       "      <th>tmin_3</th>\n",
+       "      <th>tmin_4</th>\n",
+       "      <th>tmin_5</th>\n",
+       "      <th>tmin_6</th>\n",
+       "      <th>tmin_7</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tmax_3</th>\n",
+       "      <th>tmax_4</th>\n",
+       "      <th>tmax_5</th>\n",
+       "      <th>tmax_6</th>\n",
+       "      <th>tmax_7</th>\n",
+       "      <th>tmax_8</th>\n",
+       "      <th>tmax_9</th>\n",
+       "      <th>tmax_10</th>\n",
+       "      <th>tmax_11</th>\n",
+       "      <th>tmax_12</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-122.357971 47.754948)</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3.81</td>\n",
+       "      <td>5.000</td>\n",
+       "      <td>5.59</td>\n",
+       "      <td>4.640</td>\n",
+       "      <td>10.19</td>\n",
+       "      <td>11.625</td>\n",
+       "      <td>14.50</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.26</td>\n",
+       "      <td>14.620</td>\n",
+       "      <td>18.91</td>\n",
+       "      <td>24.785</td>\n",
+       "      <td>25.74</td>\n",
+       "      <td>25.52</td>\n",
+       "      <td>19.165</td>\n",
+       "      <td>17.60</td>\n",
+       "      <td>9.450</td>\n",
+       "      <td>7.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-122.377419 47.776241)</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>3.68</td>\n",
+       "      <td>4.955</td>\n",
+       "      <td>5.56</td>\n",
+       "      <td>4.585</td>\n",
+       "      <td>10.11</td>\n",
+       "      <td>11.540</td>\n",
+       "      <td>14.41</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.25</td>\n",
+       "      <td>14.635</td>\n",
+       "      <td>18.96</td>\n",
+       "      <td>24.765</td>\n",
+       "      <td>25.73</td>\n",
+       "      <td>25.58</td>\n",
+       "      <td>19.165</td>\n",
+       "      <td>17.60</td>\n",
+       "      <td>9.475</td>\n",
+       "      <td>7.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-122.185921 47.255966)</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>3.27</td>\n",
+       "      <td>4.915</td>\n",
+       "      <td>5.66</td>\n",
+       "      <td>4.920</td>\n",
+       "      <td>9.83</td>\n",
+       "      <td>11.760</td>\n",
+       "      <td>14.66</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.04</td>\n",
+       "      <td>14.070</td>\n",
+       "      <td>18.19</td>\n",
+       "      <td>25.545</td>\n",
+       "      <td>26.81</td>\n",
+       "      <td>26.20</td>\n",
+       "      <td>18.965</td>\n",
+       "      <td>16.63</td>\n",
+       "      <td>9.120</td>\n",
+       "      <td>6.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-121.861725 47.952686)</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1.93</td>\n",
+       "      <td>3.440</td>\n",
+       "      <td>4.21</td>\n",
+       "      <td>2.920</td>\n",
+       "      <td>9.24</td>\n",
+       "      <td>10.015</td>\n",
+       "      <td>12.76</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.13</td>\n",
+       "      <td>13.765</td>\n",
+       "      <td>17.79</td>\n",
+       "      <td>24.680</td>\n",
+       "      <td>25.72</td>\n",
+       "      <td>25.52</td>\n",
+       "      <td>18.025</td>\n",
+       "      <td>15.80</td>\n",
+       "      <td>7.835</td>\n",
+       "      <td>5.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>POINT (-122.686279 45.513168)</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>5.290</td>\n",
+       "      <td>6.23</td>\n",
+       "      <td>4.825</td>\n",
+       "      <td>9.91</td>\n",
+       "      <td>12.530</td>\n",
+       "      <td>14.75</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.13</td>\n",
+       "      <td>15.765</td>\n",
+       "      <td>21.45</td>\n",
+       "      <td>28.005</td>\n",
+       "      <td>29.36</td>\n",
+       "      <td>28.66</td>\n",
+       "      <td>22.720</td>\n",
+       "      <td>19.08</td>\n",
+       "      <td>10.715</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year                       geometry  breaking leaf buds_doy  tmin_1   \n",
+       "0  2015  POINT (-122.357971 47.754948)                    50.0    3.81  \\\n",
+       "1  2015  POINT (-122.377419 47.776241)                    50.0    3.68   \n",
+       "2  2015  POINT (-122.185921 47.255966)                    56.0    3.27   \n",
+       "3  2015  POINT (-121.861725 47.952686)                    50.0    1.93   \n",
+       "4  2015  POINT (-122.686279 45.513168)                    99.0    3.70   \n",
+       "\n",
+       "   tmin_2  tmin_3  tmin_4  tmin_5  tmin_6  tmin_7  ...  tmax_3  tmax_4   \n",
+       "0   5.000    5.59   4.640   10.19  11.625   14.50  ...   14.26  14.620  \\\n",
+       "1   4.955    5.56   4.585   10.11  11.540   14.41  ...   14.25  14.635   \n",
+       "2   4.915    5.66   4.920    9.83  11.760   14.66  ...   14.04  14.070   \n",
+       "3   3.440    4.21   2.920    9.24  10.015   12.76  ...   13.13  13.765   \n",
+       "4   5.290    6.23   4.825    9.91  12.530   14.75  ...   17.13  15.765   \n",
+       "\n",
+       "   tmax_5  tmax_6  tmax_7  tmax_8  tmax_9  tmax_10  tmax_11  tmax_12  \n",
+       "0   18.91  24.785   25.74   25.52  19.165    17.60    9.450     7.37  \n",
+       "1   18.96  24.765   25.73   25.58  19.165    17.60    9.475     7.41  \n",
+       "2   18.19  25.545   26.81   26.20  18.965    16.63    9.120     6.62  \n",
+       "3   17.79  24.680   25.72   25.52  18.025    15.80    7.835     5.09  \n",
+       "4   21.45  28.005   29.36   28.66  22.720    19.08   10.715     8.00  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"/tmp/output/data.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "302e1f2b-2eaf-45a6-bcc2-e764dacb7955",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(241, 27)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(inplace = True)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e622202-d5e7-484c-a234-8c70b70ad895",
+   "metadata": {},
+   "source": [
+    "We have three matrices containing our n_samples =500 of training data:\n",
+    "\n",
+    "X. Matrix containing three fixed effect features. Dimension = 500 x 3.\n",
+    "y. Vector containing the single target variable. Dimension = 500 x 1.\n",
+    "clusters. Vector containing the cluster_id for each sample. Dimension = 500 x1. We have k = 100 unique clusters in the training data.\n",
+    "In this example, there is not an explicit Z matrix. We create one to model a random mean for each cluster. It is a matrix of all 1s with dimension = 500 x 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "27f10802-65c3-4166-8a7d-97378fbcc391",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = df.pop(\"breaking leaf buds_doy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6cbab5c1-0505-4076-b4cb-ee8f639dd35e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Z = df.pop(\"year\")\n",
+    "# Z = Z.values[:,None]\n",
+    "\n",
+    "df.drop(columns = [\"year\"], inplace = True)\n",
+    "Z = np.ones((len(y),1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e5517458-3956-47a7-9985-eb883c8536ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clusters = df.pop(\"geometry\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3eed0391-5a92-46af-afb7-09cd476618f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "indices = np.arange(len(y))\n",
+    "rand_num = 42\n",
+    "te_sz = 0.2\n",
+    "X_train, X_test, Z_train, Z_test, clusters_train, clusters_test, y_train, y_test, indices_train, indices_test = train_test_split(df, Z, clusters, y, indices, test_size=te_sz, random_state=rand_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c878ad42-e9e9-4f93-a992-40a5175076ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(192, 24)\n",
+      "(192, 1)\n",
+      "(192,)\n",
+      "(192,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_train.shape)\n",
+    "print(Z_train.shape)\n",
+    "print(clusters_train.shape)\n",
+    "print(y_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5bc6ba2a-ba29-449d-9232-5b2ee56af5b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Linear\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "lm = LinearRegression()\n",
+    "lm.fit(X_train, y_train)\n",
+    "y_hat_lm = lm.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "49dcf048-59bb-4755-a6f2-f6a1c7e04317",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4aaf89d6-bb28-4758-b0b5-6932b19b9069",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Regression Model\n",
+      "mean_absolute_error:  44.82426405323358\n",
+      "mean_squared_error:  3197.151312171903\n",
+      "r2:  0.6247992212591051\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Linear Regression Model\")\n",
+    "print(\"mean_absolute_error: \", mean_absolute_error(y_test, y_hat_lm))\n",
+    "print(\"mean_squared_error: \", mean_squared_error(y_test, y_hat_lm))\n",
+    "print(\"r2: \", r2_score(y_test, y_hat_lm))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5e7c7060-3c66-4856-ab4e-71db0447ed43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fda4efa7c10>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(y_hat_lm, y_test, \"o\")\n",
+    "plt.plot(y_test, y_test, \"-\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7fb360f7-3cf1-4fbf-9898-2527aada33ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "rf = RandomForestRegressor()\n",
+    "rf.fit(X_train, y_train)\n",
+    "y_hat_rf = rf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ad7204f6-aba8-4ba7-b9a0-501b38de83e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RF Regression Model\n",
+      "mean_absolute_error:  26.097192714315163\n",
+      "mean_squared_error:  1683.3490930514713\n",
+      "r2:  0.8024510481561682\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"RF Regression Model\")\n",
+    "print(\"mean_absolute_error: \", mean_absolute_error(y_test, y_hat_rf))\n",
+    "print(\"mean_squared_error: \", mean_squared_error(y_test, y_hat_rf))\n",
+    "print(\"r2: \", r2_score(y_test, y_hat_rf))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a13ba435-b57c-4e81-a207-9658803efe10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fda4e0af190>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(y_hat_rf, y_test, \"o\")\n",
+    "plt.plot(y_test, y_test, \"-\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cdf93e9c-e61c-4f99-ac5a-4d51e8b2f4ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from interpret.glassbox import ExplainableBoostingRegressor\n",
+    "ebm = ExplainableBoostingRegressor()\n",
+    "ebm.fit(X_train, y_train)\n",
+    "y_hat_ebm = ebm.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "99c848f0-879c-479a-aae2-5ec67878d533",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Regression Model\n",
+      "mean_absolute_error:  29.88629211922297\n",
+      "mean_squared_error:  1710.5219336949328\n",
+      "r2:  0.799262187206355\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Linear Regression Model\")\n",
+    "print(\"mean_absolute_error: \", mean_absolute_error(y_test, y_hat_ebm))\n",
+    "print(\"mean_squared_error: \", mean_squared_error(y_test, y_hat_ebm))\n",
+    "print(\"r2: \", r2_score(y_test, y_hat_ebm))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "cc521981-66a7-49a8-9e0f-f05ff2f2a5bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fdb68b47f40>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BAFLMsXcJJykkrIBiGIbmzZunl19+Wdu2bVNxcXG7zzlx4oSOHj2qgoICSdLgwYOVkZGhLVu2+K6prKzUwYMHNWLEiDC7DwBICWVO6fej/dtue45wksTCGuKZO3eu1q1bp1dffVXZ2dm+OSNOp1NdunTRqVOnVFZWpltvvVUFBQU6cuSIHnjgAfXs2VO33HKL79pZs2Zp4cKF6tGjh3Jzc7Vo0SINGDDAt6oHAAAfqiYpKayAsnr1aknSyJEj/dqfffZZzZw5U+np6Tpw4ICef/55nTx5UgUFBbrxxhv14osvKjs723f9ypUr1alTJ91+++06c+aMRo8erTVr1ig9Pb3j7wgAkBw+3S69cHNgO+EkJTgMwzDi3Ylw1dTUyOl0yuPxMB8FAJKRWdXkR69Il95oeVcQPeH8/eYsHgCAvTCkA3VwozYAAKLm0CuEE/hQQQEAxJ9ZMPnxNqn3YOv7AlsgoAAA4ouqCUwwxAMAiI+9zxFOEBQVFACA9cyCyZzdUt4V1vcFtkRAAQBYi6oJQsAQDwDAGjtXEk4QMiooAIDYMwsmP3lf6t7H+r4gIRBQAACxYxjS0gsD26maoB0M8QAAYmPzzwkniBgVFABA9JkN6Sz6RLqgl/V9QUIioAAAoqepSXqke2A7VROEiSEeAEB0bJgdGE6yLiScICJUUAAAHWc2pPOzL6Qsk3YgBAQUAEDkGs9Jj/YIbKdqgg4ioAAAIrP2VumTrf5tva6Q5u6OT3+QVAgoAIDwmQ3pPOiWMrpY3xckJQIKACB0Z+ukX+QHtjOkgygjoAAAQrP6OumrA/5t3xsp/eOrcekOkhsBBQDQPrMhnYe+kdIzrO8LUgIBBQAQXF2N9HhRYDtDOogxAgoAwNwvL5NOf+3fNnCaNPW38ekPUgoBBQAQyGxI5+FqKY0NyGENPmkAgPNOfW0eTso8hBNYigoKAKCZWTAZNlcav8z6viDlEVAAAObhZMlJyeGwvCuAxBAPAKS2k0eDD+kQThBHVFAAIFWZBZPRD0s/WGh9X4BWCCgAkIqCVU0Am2CIBwBSydcfEU6QEKigAECqMAsmE1dKQ+6yvi9AOwgoAJAKqJogwTDEAwDJ7Mu9hBMkJCooAJCszILJPzwrlUy1vi9AmAgoAJCMqJogwTHEAwDJ5LNywgmSAhUUAEgWZsHkzpeky8ZY3xeggwgoAJAMqJogyTDEAwCJ7MONhBMkJSooAJCozILJrC1S0bXW9wWIMgIKACQiqiZIcgzxAEAi2fcC4QQpgQoKACQKs2Ay+20pv7/1fQFijIACAImAqglSDEM8AGBnu/6FcIKURAUFAOzKLJj85G9S90ss7wpgtbAqKMuXL9c111yj7Oxs5eXl6eabb9ZHH33kd41hGCorK1NhYaG6dOmikSNH6tChQ37X1NfXa/78+erZs6e6deumyZMn69ixYx1/NwCQLIJVTQgnSBFhBZTy8nLNnTtXu3fv1pYtW3Tu3DmNGzdOp0+f9l3z5JNPasWKFVq1apX27Nkjl8ulsWPHqra21ndNaWmpNmzYoPXr12vnzp06deqUJk6cqMbGxui9MwBIRFvLGNIBJDkMwzAiffLXX3+tvLw8lZeX6/rrr5dhGCosLFRpaanuv/9+Sc3Vkvz8fD3xxBO655575PF41KtXL73wwguaNm2aJOn48eMqKirSG2+8oZtuuqndn1tTUyOn0ymPx6OcnJxIuw8A9mIWTBZ+LGXnW98XIAbC+fvdoUmyHk9zos/NzZUkVVRUyO12a9y4cb5rMjMzdcMNN2jXrl2SpL179+rs2bN+1xQWFqqkpMR3TWv19fWqqanx+wKApGEYwasmhBOkqIgDimEYWrBgga677jqVlJRIktxutyQpP9//X6j8/HzfY263W507d1b37t2DXtPa8uXL5XQ6fV9FRUWRdhsA7OXVedLSC/3bOmczpIOUF/Eqnnnz5un999/Xzp07Ax5zOBx+3xuGEdDWWlvXLF68WAsWLPB9X1NTQ0gBkPjMqib3H5G6dA9sB1JMRBWU+fPn67XXXtP27dvVu3dvX7vL5ZKkgEpIVVWVr6ricrnU0NCg6urqoNe0lpmZqZycHL8vAEhYjeeCD+kQTgBJYQYUwzA0b948vfzyy9q2bZuKi4v9Hi8uLpbL5dKWLVt8bQ0NDSovL9eIESMkSYMHD1ZGRobfNZWVlTp48KDvGgBIWuumSY/28G/r2Y8hHaCVsIZ45s6dq3Xr1unVV19Vdna2r1LidDrVpUsXORwOlZaWatmyZerbt6/69u2rZcuWqWvXrpo+fbrv2lmzZmnhwoXq0aOHcnNztWjRIg0YMEBjxoyJ/jsEALswq5o8UCl17mp9XwCbCyugrF69WpI0cuRIv/Znn31WM2fOlCTdd999OnPmjObMmaPq6moNHTpUmzdvVnZ2tu/6lStXqlOnTrr99tt15swZjR49WmvWrFF6enrH3g0A2NG5eumxvMB2qiZAUB3aByVe2AcFSGyNTYbeqfhWVbV1ysvO0rXFuUpPa3sifcL67Ujp+Hv+bZf8QJr5p7h0B4incP5+cxYPAEttOlippRs/UKWnztdW4MzSkkn9Nb6kII49iwGzIZ2HvpHSM6zvC5BgOM0YgGU2HazU7LX7/MKJJLk9dZq9dp82HayMU8+irL42+CodwgkQEgIKAEs0NhlauvEDmY0pe9uWbvxAjU0JN+rsb3mRtLy3f1vJrcw3AcLEEA8AS7xT8W1A5aQlQ1Klp07vVHyr4Zf2CHqdrZlVTR6ultL4f0EgXPxbA8ASVbXBw0kk19nKqargQzqEEyAiVFAAWCIvOyuq19mGWTC5cqp027PW9wVIIgQUAJa4tjhXBc4suT11pvNQHJJczuYlxwnDLJwsOSm1c/YYgPZRewRgifQ0h5ZM6i+pOYy05P1+yaT+ibEfSvWR4EM6hBMgKggoACwzvqRAq++8Wi6n/zCOy5ml1XdenRj7oJQ5pacH+bcNvZdVOkCUMcQDwFLjSwo0tr8rMXeSDVY1ARB1BBQAlktPcyTWUuKvDkmrTU5bJ5wAMUNAAYC2mFVNbvy5dMNPre8LkEIIKAAQDEM6QNwwSRYAWvtiN+EEiDMqKADQklkwmbhSGnKX9X0BUhgBBQC8qJoAtsEQDwB8vJlwAtgMFRQAqc0smNz2nHTlzZZ3BcB5BBQAqYuqCWBbDPEASD3v/wfhBLA5KigAUotZMPnRBunSUdb3BUBQBBQAqYOqCZAwGOIBkPx2ryacAAmGCgqA5GYWTH68Teo92Pq+AAgZAQVA8qJqAiQshngAJJ9tjxFOgARHBQVAcjELJnPfkXpdbn1fAESMgAIgeVA1AZIGQzwAEt9r/0Q4AZIMFRQAic0smJQelC4ssr4vAKKGgAIgMRmGtPTCwHaqJkBSIKAASDzrfih9/GZgO+EESBoEFACJxWxI56efSd16WN8XADFDQAGQGBrPSY+ahBCqJkBSIqAAsL/f3CBV7g9sJ5wASYuAAsDezIZ0Fh+TMrOt7wsAyxBQANjT2TrpF/mB7VRNgJRAQAFgP7/sK52u8m/LuUha8EF8+gPAcgQUAPZiNqTz8yqpU6b1fQEQNwQUAPZQVyM9brL7K0M6QEoioACIP7OqSe9rpR9vsb4vAGyBgAIgvszCycPfSmnp1vcFgG1wmjGA+Dj1dfATiAknQMqjggLAembB5IpJ0rS11vcFgC0RUABYyyycLDkpORyWdwWAfTHEA8Aa1Z8HH9IhnABohQoKgNgzCyZDZkkTV1jfFwAJgYACILaCVU0AoA1hD/G89dZbmjRpkgoLC+VwOPTKK6/4PT5z5kw5HA6/r2HDhvldU19fr/nz56tnz57q1q2bJk+erGPHjnXojQCwmaoPCScAIhZ2QDl9+rQGDRqkVatWBb1m/Pjxqqys9H298cYbfo+XlpZqw4YNWr9+vXbu3KlTp05p4sSJamxsDP8dALCfMqf0b/7/Y6KRiwknAEIW9hDPhAkTNGHChDavyczMlMvlMn3M4/HomWee0QsvvKAxY8ZIktauXauioiJt3bpVN910U7hdAmAnVE0AREFMVvHs2LFDeXl56tevn+6++25VVZ0/lXTv3r06e/asxo0b52srLCxUSUmJdu3aZfp69fX1qqmp8fsCYDNH9xBOAERN1APKhAkT9Ic//EHbtm3TU089pT179mjUqFGqr6+XJLndbnXu3Fndu3f3e15+fr7cbrfpay5fvlxOp9P3VVRkcqAYgPgpc0rPjPFv+7v/QzgBELGor+KZNm2a759LSko0ZMgQ9enTR6+//rqmTp0a9HmGYcgRZC+ExYsXa8GCBb7va2pqCCmAXVA1ARADMd+oraCgQH369NHhw4clSS6XSw0NDaqurva7rqqqSvn5+aavkZmZqZycHL8vAHH2yVbCCYCYiXlAOXHihI4ePaqCggJJ0uDBg5WRkaEtW84fo15ZWamDBw9qxIgRse4OgGgoc0prb/Vvu/UZwgmAqAl7iOfUqVP65JNPfN9XVFRo//79ys3NVW5ursrKynTrrbeqoKBAR44c0QMPPKCePXvqlltukSQ5nU7NmjVLCxcuVI8ePZSbm6tFixZpwIABvlU9AGyMqgkAC4QdUN59913deOONvu+9c0NmzJih1atX68CBA3r++ed18uRJFRQU6MYbb9SLL76o7Oxs33NWrlypTp066fbbb9eZM2c0evRorVmzRunpHLEO2NaB/5JemhXYTjgBEAMOwzCMeHciXDU1NXI6nfJ4PMxHAaxgVjW58yXpMqqeAEIXzt9vzuIB0DaGdADEQcwnyQJIUO/8jnACIG6ooAAIZBZMZm2Viq6xvi8AUhIBBYA/qiYAbIAhHgDNdjxOOAFgG1RQAJgHkzm7pbwrrO8LAIiAAoCqCQAbYogHSFV/WkA4AWBbVFCAVGQWTH7yvtS9j/V9AQATBBSErbHJ0DsV36qqtk552Vm6tjhX6WmOeHcLoTAMaemFge1UTQDYDAEFYdl0sFJLN36gSk+dr63AmaUlk/prfElBHHuGdr14p/ThxsB2wgkAGyKgIGSbDlZq9tp9an14k9tTp9lr92n1nVcTUuzKbEhn0SfSBb2s7wsAhIBJsghJY5OhpRs/CAgnknxtSzd+oMam4GdPNjYZevvTE3p1/5d6+9MTbV6LKGlqDD4RlnACwMaooCAk71R86zes05ohqdJTp3cqvtXwS3sEPM7QUBw8dYVUezywnSEdAAmACgpCUlUbPJy0d513aKh1wPEODW06WBmVPqKFMmdgOPnZUcIJgIRBQEFI8rKzIrouGkNDCMPZuuBDOlk51vcHACJEQEFIri3OVYEzS8EWEzvUPGRzbXGuX3s4Q0PooDKn9It8k3aqJgASDwEFIUlPc2jJpP6SFBBSvN8vmdQ/YD+UjgwNIQxmVZMH3YQTAAmLgIKQjS8p0Oo7r5bL6T+M43JmBV1iHOnQEEJ05mTwIZ2MLpZ3BwCihVU8CMv4kgKN7e8KeSdZ79CQ21NnOg/FoeaA03poCCEwCyYSVRMASYGAgrClpzlMlxIHu3bJpP6avXafHJJfSGlraAjtMAsnD30jpWdY3xcAiAGGeCAptpuoRTI0hCBqKoMP6RBOACQRKiiwZBO1cIeGYMIsmGQXSgs/tL4vABBjDsMwEm4DipqaGjmdTnk8HuXksLdDRwQ7X8cbG6hw2IRZOFlyUnIQ8AAkjnD+fjPEk8LYRC0BfPNJ8CEdwgmAJMYQTwrr6Pk6iDGzYFI0TJr1Z+v7AgAWI6CkMDZRs7FgVRMASBEM8aQwNlGzoS/3EU4AQFRQUhqbqNmMWTC5cqp027PW9wUA4owKSgqL9HwdxECwqgnhBECKIqCkODZRi7NPtzGkAwAmGOIBm6jFi1kwGTZHGr/c+r4AgM0QUCApvPN1oq2xyUi9cETVBADaREBBXFmxzb6tHPgv6aVZge2EEwDwwxwUxI13m/3Wm8W5PXWavXafNh2sjFPPYqTMGRhOxj5COAEAE1RQEBftbbPvUPM2+2P7u5JjuIchHQAICxUUxEU42+yHqrHJ0NufntCr+7/U25+esMcZQrt/TTgBgAhQQUFcRHubfVvOZTELJjevlq6abn1fACDBUEFBXERzm31bzmUJVjUhnABASAgoiAvvNvvBZpc41FwBaW+b/fbmskjNc1ksG+7Z/HOGdAAgCggoiItobbMfi7ksEStzSrv+xb/tjhcJJwAQAQIK4iYa2+xHey5LxIJVTS4fH9ufCwBJikmyiKuObrMfzbksEVn3Q+njNwPbqZoAQIcQUBB3Hdlm3zuXxe2pM52H4lBzRaa9uSwRMauazPiTVPyD6P8sAEgxDPEgoUVrLkvYgg3pEE4AICoIKEHYctMvmAp3LkuHfrer/xerdADAAgzxmLDlpl9oU6hzWTr0uzULJvfulFwDovEWYiYlT4sGkPDCrqC89dZbmjRpkgoLC+VwOPTKK6/4PW4YhsrKylRYWKguXbpo5MiROnTokN819fX1mj9/vnr27Klu3bpp8uTJOnbsWIfeSLTYctMvhMQ7l2XKVRdp+KU9TMNJRL9bwwheNbF5ONl0sFLXPbFNd/xut36yfr/u+N1uXffENj7HAGwv7IBy+vRpDRo0SKtWrTJ9/Mknn9SKFSu0atUq7dmzRy6XS2PHjlVtba3vmtLSUm3YsEHr16/Xzp07derUKU2cOFGNjY2Rv5MosN2mX4iaiH+3y3pLSy8MfFICDOkQtgEksrADyoQJE/TYY49p6tSpAY8ZhqFf/epXevDBBzV16lSVlJToueee03fffad169ZJkjwej5555hk99dRTGjNmjL7//e9r7dq1OnDggLZu3drxd9QBttr0C1EV0e+2zCk11Ppf+JO/JUQ4IWwDSHRRnSRbUVEht9utcePG+doyMzN1ww03aNeuXZKkvXv36uzZs37XFBYWqqSkxHdNa/X19aqpqfH7igXbbPqFqAvrd9t4LviQTvdLotuxGCFsA0h0UQ0obrdbkpSfn+/Xnp+f73vM7Xarc+fO6t69e9BrWlu+fLmcTqfvq6ioKJrd9on7pl+ImVB/Z1Ne6S89arInSwJUTVoibANIdDFZZuxw+E9ONAwjoK21tq5ZvHixPB6P7+vo0aNR62tL0TrADqEJZblvtJZ7h/K7PZJlctLwok8SLpxIhG0AiS+qy4xdLpek5ipJQcH5JZtVVVW+qorL5VJDQ4Oqq6v9qihVVVUaMWKE6etmZmYqMzMzml015d30a/bafXJIfuP3Md30KwWFstw3msu92/rdZqlB/zdrZuCTEjCYeMV1h10AiIKoVlCKi4vlcrm0ZcsWX1tDQ4PKy8t94WPw4MHKyMjwu6ayslIHDx4MGlCsFI0D7NC2UFaXxGIFitnv9kjW9KQLJ1Icd9gFgCgJu4Jy6tQpffLJJ77vKyoqtH//fuXm5uriiy9WaWmpli1bpr59+6pv375atmyZunbtqunTm8vnTqdTs2bN0sKFC9WjRw/l5uZq0aJFGjBggMaMGRO9d9YBHT3ADsG1t7rEIanstUOSHG1es3TjBxrb3xX276Tl73b4C98LvGDxMSkzO6zXjJZob6jmDWStq1AuNh0EkADCDijvvvuubrzxRt/3CxYskCTNmDFDa9as0X333aczZ85ozpw5qq6u1tChQ7V582ZlZ5//j/7KlSvVqVMn3X777Tpz5oxGjx6tNWvWKD09PQpvKTo6coAdggtldYm7pr7N12i5AiWS31H6mSDhJI5Vk1jtXkzYBpCoHIZhJNxGCDU1NXI6nfJ4PMrJyYl3dxCGV/d/qZ+s3x+V13r6h1dpylUXhfcks+XDUtzDyey1+wIqRt4IwdAigGQRzt9vDguEpaK5aiTs1zILJz+vims4YUM1ADBHQIGlQlnu68rJlCsnisu9qz8PvvFap9ivDmsLG6oBgDkCCiwVyuqSsslXqmxylFaglDmlpweatNtjlQ4bqgGAOQIKLBfKUu6oLPc2q5o8XG2bcCKxoRoABBPVjdqAUIWyuiTiFSiV70u/+UFgu42CiRcbqgGAOQIK4iaUpdxhL/e24SqdtrB7MQCYY4gHthCVM3eCTYS1aTjxYvdiAAhEBQVx1+FNyj7bIT0/JbDd5sGkJTZUAwB/bNQGn2hvtR6KDm9SZlY16ZYn/fRwtLoIAIiScP5+U0GBpNhttd6WUM7lafPMnWBDOgCAhMccFMTk5OBQRLxJ2YcbCScAkOSooKS4DlcxOiCiTcrMgsnFI6S73oxSrwAAdkBASXHhVDGifbpz2JuUUTUBgJTBEE+Ki+dW66Gcy1PgzNLQr/5IOAGAFENASXHx3Go9lHN53q6fqrTND/o/OGh6ROEkKnutAAAswRBPiov3VuveTcparyByObP0dv3UwCdEWDWJxyolAEDk2AcFvlU8kvlW61bsZtpyD5ZrPnpKhR8+E3hRB8JJh/ZaAQBERTh/vxnigS22WveeuTPllf6B4eT6n0YcTtpbpSQ1r1JiuAcA7IUhHkiyyVbrMZgIG89VSgCAyBFQ4BP2ycHR8uKdzZuvtRaFVTrxXKUEAIgcAQXxZVY1+fsV0jWzQn6Jts4QiucqJQBA5AgoiJ8oDOm0tzon3quUAACRYZIsrPeb66MWToKdIXTv2n16euvH+tP7x/XDa4p82/a35P1+yaT+1s61AQC0iwoKrGUWTKb9QbpiYlgvE8rqnJVbD/vaLuyaIUk6+d1ZX5vLmaWH/v4KObt01qv7v4zPxGAAgCkCCqwTxVU67a3Oac3zP8Hkn8f00yU9uyovO0vVpxv06Ots3gYAdsQQD2JvWe+oLyEOd9WNt6qyfs8XmjiwUJ4zDZq7znx4aPbafdp0sDLivgEAOo6Agtgqc0oNtf5td/25w0uII1l1493zZPenJ9i8DQBsjoCC2DCM4FWTi4d1+OXbOwm5LW9/9k3Im7cBAOKDgILoK3NKSy80ae/4xmtebZ2E3L7QnsHmbQAQPwQURJdZ1WTO7qiGE69gZwgF41DzJNhQd8tl8zYAiB9W8SAkbe3WKklqapQeMdnsLAbBpKXWZwgd+eY7/Wrrx5LMT2ZeMqm/hn2vB5u3AYDNEVDQrvZ2azWtmkgxDyderc8Qutx1QUB/Xa2WDy+Z1F+z1+6TQ8GDDPuhAED8OAzDSLilCjU1NXI6nfJ4PMrJyYl3d5Kad7fW1h8S75/uiqzpgU9a8KGUUxjrrrWp3YqPQgheAICoCufvNxWUJBbKH+m2ntPzgkyVvXbIdBiksxr0UdbMwAcsqpq0J5STmVsPD7GTLADYBwElSUVSHTB7jpkjZlUTyTbhJByhBJloiyQ4AkCqIaAkoWDDMt5dUlffeXVASAn2nNbMwsnrf7dbf3/tFR3rdIpgWAkAQsMy4yQTyiF6rXdJbes5Xk6dMg0nl9StU26PvJD79vanJ/Tq/i/19qcnUm6n1rZOX2Z7fQDwRwUlybR3iF7LXVK9QxvtPSfYkM4ldetUEOJy3FSvHLQXHB1qDo5j+7sY7gEAUUFJOqHuftryuraeYxZOLq9bo0vq1kkKbTkulYPwgiMAgICSdELd/bTldWbP6aWTQYd06tVZkjTrf13SbvUjkiGnZBRJcASAVEZASTLtHaLn3e695bBM6+ccyZquPVlz/J531kj3VU28xvR3tdsfKgfNIgmOAJDKCChJpq1D9ILtktryOWZVk+K6tepb/4Lf64Q694TKQbNIgiMApDICShIKdohefk6mSsf0Vf25poBVNOMvajDdFfaSunUyWnxMwt0KPpEqB7FcZRRJcASAVMZW90ms5YZgR775Tn985wu5a0xW0fzX/xPw3BO5V2tt/98Gf06IK28amwxd98S2dg/m23n/qLj+cbZqlVGqr2YCkNrC+ftNQEkBbZ2nY3qWzpKTkqM5LERj11Pvz5fMD+Yz2zjOSu2dNxTt/rGTLIBURUCBj7eC0Xqi6uWOL/TnzJ8FPiFG29XbtXIQ7P542aXCAwDJgMMC4WO2isZsIuw3fSaq5//+Q8Q/p72qgF0P5otkYzsAQOwRUJJc69UxwfY2efr7V2lKhD8j1OpIPA7maw+rjADAnqK+iqesrEwOh8Pvy+U6v1+GYRgqKytTYWGhunTpopEjR+rQoUPR7gb+h3d1zEDHp0HDScvrwpXou8Qm0iojAEglMVlmfOWVV6qystL3deDAAd9jTz75pFasWKFVq1Zpz549crlcGjt2rGpra2PRlZR3bXGujmRN12uZD/m1P3X2H3RJ3bqA/TfCWWqbDLvEsj8JANhTTIZ4OnXq5Fc18TIMQ7/61a/04IMPaurUqZKk5557Tvn5+Vq3bp3uueeeWHQnpaU/cmFAW8sdYQ2d338j3Ims8Z6/EY3VMN79SWav3SeHzFcZsT9J6mCFFWAfMQkohw8fVmFhoTIzMzV06FAtW7ZM3/ve91RRUSG3261x48b5rs3MzNQNN9ygXbt2BQ0o9fX1qq+v931fU1MTi24nly92S/9+U0Bz6+3qL+yaISn4UlvvUI3ZUtt4zt+I5qog78Z2rV/PZYNVRrCOXVeaAakq6gFl6NChev7559WvXz999dVXeuyxxzRixAgdOnRIbrdbkpSfn+/3nPz8fH3++edBX3P58uVaunRptLuavMqcAU1/u/aXuvmtiwLaPd+d1b1r9+nCrhlBh2ocah6qGdvf5fd/k/GavxFJmGqPXVcZwRqx+EwB6Jioz0GZMGGCbr31Vg0YMEBjxozR66+/Lql5KMfL4fD/j75hGAFtLS1evFgej8f3dfTo0Wh3O3mYhJPGh0/q3r9d2uZckZPfnQ36ksEO9IvH/I1YznvxrjKactVFGn5pD8JJikiGuVRAMor5WTzdunXTgAEDdPjwYd+8FG8lxauqqiqgqtJSZmamcnJy/L7QysebTcOJyjztzhUJVeuhmnicL8PpyIg2PlOAPcU8oNTX1+vDDz9UQUGBiouL5XK5tGXLFt/jDQ0NKi8v14gRI2LdleRV5pTW3ebfNv0/fbvCRmsOiNlQTbCDCV3OrJiUxdm3BNHGZwqwp6jPQVm0aJEmTZqkiy++WFVVVXrsscdUU1OjGTNmyOFwqLS0VMuWLVPfvn3Vt29fLVu2TF27dtX06SZnwqB9QaomLXV0Doh3u/dgQzWhzt+IxgoJ9i1BtPGZAuwp6gHl2LFjuuOOO/TNN9+oV69eGjZsmHbv3q0+ffpIku677z6dOXNGc+bMUXV1tYYOHarNmzcrOzs72l1Jbh+8Kv3HPwa2m5yl450r0taJwt0yO+lU/TnTx6T2h2ra2yU2WiskQnkvbYUpoDU+U4A9cVhgIjKrmvx4m9R7cNCnBDtRuD3RWGYZ7dOC7X46MhIPnynAGuH8/Y75HBREWbAhnTbCiRR8rkhb/nlMX+28f1SH/sMcixUSVs97QfLjMwXYDxWURLH3OWnjPwW2mwzptMU7D8RdU6dH/3RI354Ovry4wJmlnfeP6tAqnLc/PaE7fre73ev+ePewsHebZddPRBufKSC2wvn7zWnGicCsajJnt5R3Rdgv5Z0r8vanJ9oMJ1Lz0spV2w7rJ2P6hf1zvGK5QiJapyMnyx+lZHkf8WTHE7eBVEVAsbsQVulEItRAsHLrYV3uyo64xG33FRLJsr15srwPAPBiDopd7V8Xs3AihRcIOrKLpp1PC/ZOjGy9SZd3e/NNBys7/DPCOR06Ula8DwCwGhUUOzILJgs+lHIKo/YjvMEhlB1mO3IisV1PC25v8m6w84fCYUVVw4r3AQDxQAXFTgwjeNUkiuFE8t+mPhQd2UXTjiskYr29uVVVDbZpB5CsqKDYxSd/kdZO9W+7/O+kO/4Ysx85vqRA/zymn1Zu/bjdazs6R8RupwXHcvKulVUNtmkHkKwIKHbw7xOkL3b5ty3+Usq8IOY/et6oy/THdz6Xu6be9PFo7qJppxUSsZy8G05Vo6P3w+6TkAEgUgzxxJN3SKd1OCnzWBJOpObQUDb5Sjlk3YnEdhDLybtWVjXsPAkZADqCgBIvx/ZKSy/0b7vlN1FbpRMOO84RibWWc3CiHcysrGrE8n0AQDyxk2w8PHOTdLTV7qoPfSOlZ8SnP/8jFTf6isVKm8YmQ9c9sa3dw+c6uktvS+yDAiARhPP3m4BipaYm6ZHu/m3ZhdLCD+PTH0iKTTCLx+FzqRgwASQWAoodHflvac3f+bfd+ZJ02Zj49AcxR1UDAPxxFo/d/NtwqeoD/7aHv5XS0uPTnxji/+LPs9vSagBIJASUWGpqlB5ptXqi1xXS3PZP901EVAwC2WlpNQAkElbxxMonfwkMJzPfSOpwwnkwAIBooYISC7+5Xqr8m3/bw9VSWnLmQc6DAQBEW3L+xYyXpkbpl5f5h5Oioc17myRpOJE4DwYAEH1UUKLl64+lf73Gv+3Hf5F6D4lPfyzEeTAAgGgjoETDX5+S/vLI+e8Lvy/dvV1ypMZwBufBAACijYDSEY3npMcvls6ePt92y2+lQdPi16c48J4H097OqZwHAwAIVfJOjIi1rw5Jj/bwDycLP065cCJxHgwAIPoIKJHYvkxaPeL89xePkJaclLLz49aleEvFAwcBALHDEE84zjVIj+XJ73SV29ZIV94Srx7ZCjunAgCihYASqsq/Ne9v0tJPP5W69YxPf2yKnVMBANHAEE87GpsMffkfP/UPJ9+7sXlvE8IJAAAxQQWlDX/+2+e6acNAXdSi7b5OP9Ooq2dqfNx6BQBA8qOCEsTb5Zt004aBfm2D6n6r/zw1kLNlAACIMQKKiaY3fqrh288vF97cOFiX1K2TRxf4pscu3fiBGpvMdv0AAAAdxRBPa09+T2nfnfB9O7PhPu1ousrvkpZnyzAhFACA6COgtFRXI7UIJwPqfq9adQ16OWfLAAAQGwSUlrJypNvW6PDxExr7l/Y3FuNsGQAAYoM5KK1deYu+N3qWCpxZAdu2ezkkFXC2DAAAMUNAMcHZMgAAxBcBJQjOlgEAIH6Yg9IGzpYBACA+CCjt4GwZAACsxxAPAACwHQIKAACwHQIKAACwHQIKAACwHQIKAACwHQIKAACwHQIKAACwHQIKAACwHQIKAACwnYTcSdYwDElSTU1NnHsCAABC5f277f073paEDCi1tbWSpKKiojj3BAAAhKu2tlZOp7PNaxxGKDHGZpqamnT8+HFlZ2fL4eDgvrbU1NSoqKhIR48eVU5OTry7kzC4b5Hj3kWG+xY57l3krL53hmGotrZWhYWFSktre5ZJQlZQ0tLS1Lt373h3I6Hk5OTwL24EuG+R495FhvsWOe5d5Ky8d+1VTryYJAsAAGyHgAIAAGyHgJLkMjMztWTJEmVmZsa7KwmF+xY57l1kuG+R495Fzs73LiEnyQIAgORGBQUAANgOAQUAANgOAQUAANgOAQUAANgOASUJlJWVyeFw+H25XC7f44ZhqKysTIWFherSpYtGjhypQ4cOxbHH8fPWW29p0qRJKiwslMPh0CuvvOL3eCj3qr6+XvPnz1fPnj3VrVs3TZ48WceOHbPwXVivvfs2c+bMgM/gsGHD/K5Jxfu2fPlyXXPNNcrOzlZeXp5uvvlmffTRR37X8JkzF8q943MXaPXq1Ro4cKBv47Xhw4frzTff9D2eSJ83AkqSuPLKK1VZWen7OnDggO+xJ598UitWrNCqVau0Z88euVwujR071nemUSo5ffq0Bg0apFWrVpk+Hsq9Ki0t1YYNG7R+/Xrt3LlTp06d0sSJE9XY2GjV27Bce/dNksaPH+/3GXzjjTf8Hk/F+1ZeXq65c+dq9+7d2rJli86dO6dx48bp9OnTvmv4zJkL5d5JfO5a6927tx5//HG9++67evfddzVq1ChNmTLFF0IS6vNmIOEtWbLEGDRokOljTU1NhsvlMh5//HFfW11dneF0Oo1f//rXFvXQniQZGzZs8H0fyr06efKkkZGRYaxfv953zZdffmmkpaUZmzZtsqzv8dT6vhmGYcyYMcOYMmVK0Odw35pVVVUZkozy8nLDMPjMhaP1vTMMPneh6t69u/H73/8+4T5vVFCSxOHDh1VYWKji4mL98Ic/1GeffSZJqqiokNvt1rhx43zXZmZm6oYbbtCuXbvi1V1bCuVe7d27V2fPnvW7prCwUCUlJSl/P3fs2KG8vDz169dPd999t6qqqnyPcd+aeTweSVJubq4kPnPhaH3vvPjcBdfY2Kj169fr9OnTGj58eMJ93ggoSWDo0KF6/vnn9ec//1m/+93v5Ha7NWLECJ04cUJut1uSlJ+f7/ec/Px832NoFsq9crvd6ty5s7p37x70mlQ0YcIE/eEPf9C2bdv01FNPac+ePRo1apTq6+slcd+k5rH/BQsW6LrrrlNJSYkkPnOhMrt3Ep+7YA4cOKALLrhAmZmZuvfee7Vhwwb1798/4T5vCXmaMfxNmDDB988DBgzQ8OHDdemll+q5557zTRhzOBx+zzEMI6ANzSK5V6l+P6dNm+b755KSEg0ZMkR9+vTR66+/rqlTpwZ9Xirdt3nz5un999/Xzp07Ax7jM9e2YPeOz525yy+/XPv379fJkyf10ksvacaMGSovL/c9niifNyooSahbt24aMGCADh8+7FvN0zr5VlVVBaToVBfKvXK5XGpoaFB1dXXQayAVFBSoT58+Onz4sCTu2/z58/Xaa69p+/bt6t27t6+dz1z7gt07M3zumnXu3FmXXXaZhgwZouXLl2vQoEF6+umnE+7zRkBJQvX19frwww9VUFCg4uJiuVwubdmyxfd4Q0ODysvLNWLEiDj20n5CuVeDBw9WRkaG3zWVlZU6ePAg97OFEydO6OjRoyooKJCUuvfNMAzNmzdPL7/8srZt26bi4mK/x/nMBdfevTPD586cYRiqr69PvM+bpVNyERMLFy40duzYYXz22WfG7t27jYkTJxrZ2dnGkSNHDMMwjMcff9xwOp3Gyy+/bBw4cMC44447jIKCAqOmpibOPbdebW2t8d577xnvvfeeIclYsWKF8d577xmff/65YRih3at7773X6N27t7F161Zj3759xqhRo4xBgwYZ586di9fbirm27lttba2xcOFCY9euXUZFRYWxfft2Y/jw4cZFF12U8vdt9uzZhtPpNHbs2GFUVlb6vr777jvfNXzmzLV37/jcmVu8eLHx1ltvGRUVFcb7779vPPDAA0ZaWpqxefNmwzAS6/NGQEkC06ZNMwoKCoyMjAyjsLDQmDp1qnHo0CHf401NTcaSJUsMl8tlZGZmGtdff71x4MCBOPY4frZv325ICviaMWOGYRih3aszZ84Y8+bNM3Jzc40uXboYEydONL744os4vBvrtHXfvvvuO2PcuHFGr169jIyMDOPiiy82ZsyYEXBPUvG+md0zScazzz7ru4bPnLn27h2fO3N33XWX0adPH6Nz585Gr169jNGjR/vCiWEk1ufNYRiGYV29BgAAoH3MQQEAALZDQAEAALZDQAEAALZDQAEAALZDQAEAALZDQAEAALZDQAEAALZDQAEAALZDQAEAALZDQAEAALZDQAEAALZDQAEAALbz/wOrnrXRSjdEdAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(y_hat_ebm, y_test, \"o\")\n",
+    "plt.plot(y_test, y_test, \"-\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "364941dc-5d93-4cf7-9638-a59dac665ee8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from merf import MERF\n",
+    "merf = MERF()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f5facaa7-7086-4f56-8346-98765d508c19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO     [merf.py:307] Training GLL is 1763.3817123147687 at iteration 1.\n",
+      "INFO     [merf.py:307] Training GLL is 1870.6218704909504 at iteration 2.\n",
+      "INFO     [merf.py:307] Training GLL is 1907.8271937117267 at iteration 3.\n",
+      "INFO     [merf.py:307] Training GLL is 1917.7421474746807 at iteration 4.\n",
+      "INFO     [merf.py:307] Training GLL is 1940.1322305242377 at iteration 5.\n",
+      "INFO     [merf.py:307] Training GLL is 1954.298382232997 at iteration 6.\n",
+      "INFO     [merf.py:307] Training GLL is 1963.5869201183036 at iteration 7.\n",
+      "INFO     [merf.py:307] Training GLL is 1958.6200378598992 at iteration 8.\n",
+      "INFO     [merf.py:307] Training GLL is 1958.6875917054062 at iteration 9.\n",
+      "INFO     [merf.py:307] Training GLL is 1974.6210287979754 at iteration 10.\n",
+      "INFO     [merf.py:307] Training GLL is 1962.9141082223468 at iteration 11.\n",
+      "INFO     [merf.py:307] Training GLL is 1979.9544334991142 at iteration 12.\n",
+      "INFO     [merf.py:307] Training GLL is 1974.629933478824 at iteration 13.\n",
+      "INFO     [merf.py:307] Training GLL is 1975.1997896158846 at iteration 14.\n",
+      "INFO     [merf.py:307] Training GLL is 1996.8425550183122 at iteration 15.\n",
+      "INFO     [merf.py:307] Training GLL is 1996.5776671569074 at iteration 16.\n",
+      "INFO     [merf.py:307] Training GLL is 1996.0038768111663 at iteration 17.\n",
+      "INFO     [merf.py:307] Training GLL is 2007.4828706103815 at iteration 18.\n",
+      "INFO     [merf.py:307] Training GLL is 1997.2774818986284 at iteration 19.\n",
+      "INFO     [merf.py:307] Training GLL is 1998.261373869593 at iteration 20.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<merf.merf.MERF at 0x7fdb68ab21a0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merf.fit(X_train, Z_train, clusters_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ead4f880-c746-4d03-9c49-98021a70b6f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat_merf = merf.predict(X_test, Z_test, clusters_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "46eaa788-adac-4e83-ba75-bde15f17d1db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MERF Regression Model\n",
+      "mean_absolute_error:  22.68994763980058\n",
+      "mean_squared_error:  1460.4859699626766\n",
+      "r2:  0.8286050862891772\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"MERF Regression Model\")\n",
+    "print(\"mean_absolute_error: \", mean_absolute_error(y_test, y_hat_merf))\n",
+    "print(\"mean_squared_error: \", mean_squared_error(y_test, y_hat_merf))\n",
+    "print(\"r2: \", r2_score(y_test, y_hat_merf))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f2d30fb2-779c-4030-9ffa-f9a9d83d1217",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fdb68b65300>]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(y_hat_merf, y_test, \"o\")\n",
+    "plt.plot(y_test, y_test, \"-\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "42129b7b-6f75-41e7-b565-cf44a175dadf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO     [merf.py:307] Training GLL is 1756.0443098578774 at iteration 1.\n",
+      "INFO     [merf.py:307] Training GLL is 1870.6299808880237 at iteration 2.\n",
+      "INFO     [merf.py:307] Training GLL is 1912.2472198141095 at iteration 3.\n",
+      "INFO     [merf.py:307] Training GLL is 1929.4846161297685 at iteration 4.\n",
+      "INFO     [merf.py:307] Training GLL is 1950.4886212027704 at iteration 5.\n",
+      "INFO     [merf.py:307] Training GLL is 1965.556734921893 at iteration 6.\n",
+      "INFO     [merf.py:307] Training GLL is 1966.2954209093828 at iteration 7.\n",
+      "INFO     [merf.py:307] Training GLL is 1958.6409588405618 at iteration 8.\n",
+      "INFO     [merf.py:307] Training GLL is 1959.456316351379 at iteration 9.\n",
+      "INFO     [merf.py:307] Training GLL is 1980.5484296238556 at iteration 10.\n",
+      "INFO     [merf.py:307] Training GLL is 1974.6592953690306 at iteration 11.\n",
+      "INFO     [merf.py:307] Training GLL is 1972.7337181526273 at iteration 12.\n",
+      "INFO     [merf.py:307] Training GLL is 1972.2167235724894 at iteration 13.\n",
+      "INFO     [merf.py:307] Training GLL is 1969.0154173477276 at iteration 14.\n",
+      "INFO     [merf.py:307] Training GLL is 1985.8735022996786 at iteration 15.\n",
+      "INFO     [merf.py:307] Training GLL is 1991.5518261299442 at iteration 16.\n",
+      "INFO     [merf.py:307] Training GLL is 2013.8915582551788 at iteration 17.\n",
+      "INFO     [merf.py:307] Training GLL is 2010.7922731798976 at iteration 18.\n",
+      "INFO     [merf.py:307] Training GLL is 2004.071512798519 at iteration 19.\n",
+      "INFO     [merf.py:307] Training GLL is 2011.8739566409765 at iteration 20.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<merf.merf.MERF at 0x7fdb688b1c90>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrf_rf = MERF(rf)\n",
+    "mrf_rf.fit(X_train, Z_train, clusters_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8f23720e-b2fd-4d7a-82dc-7a9aaa45d94a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat_merf_rf = mrf_rf.predict(X_test, Z_test, clusters_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "244d858c-8288-4b3e-9cc3-a8ae0ec386a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MERF+RF Regression Model\n",
+      "mean_absolute_error:  23.526416819017854\n",
+      "mean_squared_error:  1513.2422472646606\n",
+      "r2:  0.8224138884400743\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"MERF+RF Regression Model\")\n",
+    "print(\"mean_absolute_error: \", mean_absolute_error(y_test, y_hat_merf_rf))\n",
+    "print(\"mean_squared_error: \", mean_squared_error(y_test, y_hat_merf_rf))\n",
+    "print(\"r2: \", r2_score(y_test, y_hat_merf_rf))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "028bf8f6-fe99-409a-9066-2c6bdeab6e13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO     [merf.py:307] Training GLL is 2437.4899014848297 at iteration 1.\n",
+      "INFO     [merf.py:307] Training GLL is 2427.8988250485686 at iteration 2.\n",
+      "INFO     [merf.py:307] Training GLL is 2434.7665505313316 at iteration 3.\n",
+      "INFO     [merf.py:307] Training GLL is 2438.171820183171 at iteration 4.\n",
+      "INFO     [merf.py:307] Training GLL is 2440.0531107244506 at iteration 5.\n",
+      "INFO     [merf.py:307] Training GLL is 2441.3670563152627 at iteration 6.\n",
+      "INFO     [merf.py:307] Training GLL is 2442.47436043523 at iteration 7.\n",
+      "INFO     [merf.py:307] Training GLL is 2443.4993851274453 at iteration 8.\n",
+      "INFO     [merf.py:307] Training GLL is 2444.4805964485568 at iteration 9.\n",
+      "INFO     [merf.py:307] Training GLL is 2445.426522080128 at iteration 10.\n",
+      "INFO     [merf.py:307] Training GLL is 2446.335611113075 at iteration 11.\n",
+      "INFO     [merf.py:307] Training GLL is 2447.203061949204 at iteration 12.\n",
+      "INFO     [merf.py:307] Training GLL is 2448.0230791132976 at iteration 13.\n",
+      "INFO     [merf.py:307] Training GLL is 2448.789573847746 at iteration 14.\n",
+      "INFO     [merf.py:307] Training GLL is 2449.496363235542 at iteration 15.\n",
+      "INFO     [merf.py:307] Training GLL is 2450.1372283921314 at iteration 16.\n",
+      "INFO     [merf.py:307] Training GLL is 2450.705949842018 at iteration 17.\n",
+      "INFO     [merf.py:307] Training GLL is 2451.1963552777793 at iteration 18.\n",
+      "INFO     [merf.py:307] Training GLL is 2451.602387485692 at iteration 19.\n",
+      "INFO     [merf.py:307] Training GLL is 2451.918191600278 at iteration 20.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<merf.merf.MERF at 0x7fdb6879e230>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrf_lm = MERF(lm)\n",
+    "mrf_lm.fit(X_train, Z_train, clusters_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ca5689b7-0ada-40a8-99e9-d6f8003231b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat_merf_lm = mrf_lm.predict(X_test, Z_test, clusters_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "0fb47c77-cc8e-4f2f-b62a-5d36bf3fc039",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MERF+LM Regression Model\n",
+      "mean_absolute_error:  41.65389016189721\n",
+      "mean_squared_error:  2853.789317204953\n",
+      "r2:  0.6650943700720995\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"MERF+LM Regression Model\")\n",
+    "print(\"mean_absolute_error: \", mean_absolute_error(y_test, y_hat_merf_lm))\n",
+    "print(\"mean_squared_error: \", mean_squared_error(y_test, y_hat_merf_lm))\n",
+    "print(\"r2: \", r2_score(y_test, y_hat_merf_lm))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "d66c0f33-74c2-4ad3-96f7-ce1740b3ed08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fdb689bb0d0>]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ddPLkSRUXF+vWW29Vbm6uDh06pAceeEDdunXTLbfc4r92xowZmjt3rrp27ars7GzNmzdPffv29e/qAQAgbIYhLfySua3b5dKs7ZZ0B5EJK6CsWLFCkjRs2DBT+1NPPaXp06crNTVVe/fu1TPPPKMTJ04oNzdXN954o55//nllZmb6r1+2bJk6dOig2267TadPn9aIESO0cuVKpaamRv6KAADJp+J/pf/venPbtFelguuDXw/bcxmGYVjdiXBVV1fL7XbL5/OxHgUAkt2zt0jvbTS3PVQlpXAert2E8/nNWTwAAOdivUnCIl4CAJyJcJLQCCgAAOchnCQ8AgoAwDk+Ox4YTkb9lHCSgFiDAgBwhi2/ljYsMLfdf0jq1CXY1XA4AgoAwP6Y0kk6TPEAAOyNcJKUCCgAAPsinCQtAgoAwH4aGwLDyWUjCSdJhDUoAAB7eX+T9MxEc9tdWyRPX0u6A2sQUAAA9vGbr0nH3ze3LTghuVyWdAfWIaAAAOyB9SY4D2tQAADWI5ygGQIKAMBahBMEQUABAFjjxOHAcHLL7wknkMQaFACAFV6/X/rb78xtD3qltE7W9Ae2Q0ABAMQXUzoIAVM8AID4IZwgRAQUAEB8EE4QBgIKACC2ztQGhpP+3yWcoFWsQQEAxE7ZC9Kfv2duu3eP1KWXNf2BYxBQAACxwZQOIsAUDwAg+ggniBABBQAQXYQTRAEBBQAQPc3DSVZPwgnahTUoAIDIfbxfWjHY3Hb7i9JlI6zpDxyPgAIAiMwfp0gH1prbHjoupaRa0x8kBAIKAKD9WG+CGGENCgCgfQgniCECCgAgfIQTxBgBBQAQulpfYDgZ/mPCCaKONSgAgNC89bj0l/nmth++L3Xuak1/kNAIKACAtjGlgzhjigcA0DrCCSxAQAEAtIxwAosQUAAAgRobA8PJxd8gnCBuWIMCADD7YKv01Fhz2x1vShddY01/kJQIKACAcx4fIlXuM7ctOCG5XJZ0B8mLgAIAOIv1JrAR1qAAAAgnsB0CCgAkO8IJbIiAAgDJqroiMJxMWE44gS2wBgUAktEbP5G2/sbc9sBRKb2zNf0BmiGgAECyYUoHDsAUDwAkE8IJHIKAAgDJgnACByGgAECi+7w+MJxc/X8IJ7C1sALK4sWLde211yozM1M9evTQzTffrAMHDpiuMQxDxcXFysvLU6dOnTRs2DDt22euSlhXV6fZs2erW7du6ty5syZMmKAjR45E/moAAGb7X5F+3t3cNnuXdPPj1vQHCFFYAaW0tFT33HOPtm3bpvXr1+vzzz/X6NGjderUKf81S5Ys0dKlS7V8+XJt375dHo9Ho0aNUk1Njf+aoqIirVmzRiUlJdqyZYtOnjypcePGqaGhIXqvDACS3S9ypT9929xW7JO6XmpNf4AwuAzDMNr75E8++UQ9evRQaWmprr/+ehmGoby8PBUVFen++++XdHa0JCcnR4888ojuvPNO+Xw+de/eXc8++6wmT54sSTp69Kjy8/O1du1a3XTTTW3+3urqarndbvl8PmVlZbW3+wCQuFhvAhsK5/M7ojUoPt/ZN3t2drYkqby8XF6vV6NHj/Zfk5GRoRtuuEFbt26VJO3cuVNnzpwxXZOXl6fCwkL/Nc3V1dWpurra9AUAaAHhBAmg3QHFMAzNmTNHQ4cOVWFhoSTJ6/VKknJyckzX5uTk+B/zer1KT09Xly5dWrymucWLF8vtdvu/8vPz29ttAEhszcPJBV0JJ3CkdgeUWbNmac+ePfrjH/8Y8Jir2bHchmEEtDXX2jXz58+Xz+fzfx0+fLi93QaAxPTJPwPDyZTnpfvet6Y/QITaVUl29uzZeuWVV7R582b17NnT3+7xeCSdHSXJzc31t1dWVvpHVTwej+rr61VVVWUaRamsrNSQIUOC/r6MjAxlZGS0p6sAkPj+33elfS+a235yTEqlWDicK6wRFMMwNGvWLL344ovauHGjCgoKTI8XFBTI4/Fo/fr1/rb6+nqVlpb6w0f//v2VlpZmuqaiokJlZWUtBhQAQAuK3YHhpNhHOIHjhfUOvueee7R69Wq9/PLLyszM9K8Zcbvd6tSpk1wul4qKirRo0SL17t1bvXv31qJFi3TBBRdo6tSp/mtnzJihuXPnqmvXrsrOzta8efPUt29fjRw5MvqvEAASFYthkcDCCigrVqyQJA0bNszU/tRTT2n69OmSpPvuu0+nT5/W3XffraqqKg0cOFBvvPGGMjMz/dcvW7ZMHTp00G233abTp09rxIgRWrlypVJTUyN7NQCQLAgnSHAR1UGxCnVQAGs1NBp6u/y4Kmtq1SOzo64ryFZqSusL4REldTXS4p7mtuvvk4Y/aE1/gDCE8/nNJCWAsKwrq9DCV/erwlfrb8t1d9SC8X00pjC3lWciYtv/IL0219w276B0YQ9r+gPEEAEFQMjWlVVo5qpdaj7s6vXVauaqXVpx+zWElFhhSgdJhtOMAYSkodHQwlf3B4QTSf62ha/uV0Oj42aN7Y9wgiREQAEQkrfLj5umdZozJFX4avV2+fH4dSoZEE6QpAgoAEJSWdNyOGnPdWiDYQSGk4sGEE6QNFiDAiAkPTI7RvU6tOL9UumZCea2aa9KBddb0x/AAgQUACG5riBbue6O8vpqg65DcUnyuM9uOUYEllwifXbM3LbghNTGeWZAomGKB0BIUlNcWjC+j6SzYeR8Td8vGN+HeiiRKHYHhpNiH+EESYmAAiBkYwpzteL2a+Rxm6dxPO6ObDGOFIthAROmeACEZUxhrkb18VBJNpoIJ0AAAgqAsKWmuDT40q5Wd8P5qo9KS79ibhuxQPrGHGv6A9gIAQUArPDKv0u7nja3/ehDqWOQ0RQgCRFQACDemNIB2sQiWQCIJ8IJEBICCgDEC+EECBkBBQBireFMYDj58hDCCdAK1qAAQCzt/bP0wgxz213/I3kKrekP4BAEFACIFaZ0gHZjigcAYoFwAkSEgAIA0UY4ASJGQAGAaCKcAFFBQAGAaKj8R2A4uXkF4QRoJxbJAkCknpkovb/J3PbjSqlDhiXdARIBAQUAIsGUDhATTPEAQHsRToCYIaAAQHsQToCYIqAAQDjqagLDSb+phBMgyliDAgCh+p/fSOt/Ym4rKpO+lG9Nf4AERkABgFAwpQPEFVM8ANAWwgkQdwQUAGgN4QSwBAEFAIIxjMBwkn4h4QSIE9agAEBzH2yVnhprbrv9Bemykdb0B0hCBBQAON+yvpLvQ3PbQ1VSCgPOQDwRUACgCetNANvgTwIAkAgngM0QUACAcALYDgEFQPKq+TgwnNxwP+EEsAHWoABITq/fL/3td+a2+8qlC7Kt6Q8AEwIKgOTDlA5ge0zxAEguhBPAEQgoAJIH4QRwDAIKgMTX8HlgOMntRzgBbIw1KAAS2/6XpT99x9z2/VIp72pLugMgNAQUAImLKR3AsZjiAZCYCCeAoxFQACQewgngeAQUAImFcAIkhLADyubNmzV+/Hjl5eXJ5XLppZdeMj0+ffp0uVwu09egQYNM19TV1Wn27Nnq1q2bOnfurAkTJujIkSMRvRAASe7TdwPDyfjHCCeAQ4UdUE6dOqV+/fpp+fLlLV4zZswYVVRU+L/Wrl1reryoqEhr1qxRSUmJtmzZopMnT2rcuHFqaGgI/xUAwOpvScv7m9se9Er9p1vSHQCRC3sXz9ixYzV27NhWr8nIyJDH4wn6mM/n05NPPqlnn31WI0eOlCStWrVK+fn52rBhg2666aZwuwQgmTGlAySkmKxB2bRpk3r06KHLL79cd9xxhyorK/2P7dy5U2fOnNHo0aP9bXl5eSosLNTWrVuD/ry6ujpVV1ebvgCAcAIkrqgHlLFjx+q5557Txo0b9eijj2r79u0aPny46urqJEler1fp6enq0qWL6Xk5OTnyer1Bf+bixYvldrv9X/n5+dHuNgCnIZwACS3qhdomT57s/3dhYaEGDBigXr166bXXXtOkSZNafJ5hGHK5XEEfmz9/vubMmeP/vrq6mpACJKv6U9KiPHPbV2+R/m2lJd0BEBsxrySbm5urXr166eDBg5Ikj8ej+vp6VVVVmUZRKisrNWTIkKA/IyMjQxkZGbHuKgC7+9vvpdd/aG77991SdoEl3QEQOzGvg3Ls2DEdPnxYubm5kqT+/fsrLS1N69ev919TUVGhsrKyFgMKAKjYHRhOin2EEyBBhT2CcvLkSb377rv+78vLy7V7925lZ2crOztbxcXFuvXWW5Wbm6tDhw7pgQceULdu3XTLLbdIktxut2bMmKG5c+eqa9euys7O1rx589S3b1//rh4AMGG9CZB0wg4oO3bs0I033uj/vmltyLRp07RixQrt3btXzzzzjE6cOKHc3FzdeOONev7555WZmel/zrJly9ShQwfddtttOn36tEaMGKGVK1cqNTU1Ci8JQEIhnABJyWUYhmF1J8JVXV0tt9stn8+nrKwsq7sDIBYMQ1r4JXObK0VaUGVJdwBELpzP75gvkgWAsB3eLj3ZbMp3Sol0RetFIgEkDgIKAHtZfp306QFz20PHpRSmgIFkQkABYB+sNwHwhZhvMwaAkBBOAJyHgALAeoQTAM0QUABY59SngeHk6/cSTgCwBgWARdY/JP3PY+a2H74nde5mTX8A2AoBBUD8MaUDoA1M8QCIL8IJgBAwgoKYaWg09Hb5cVXW1KpHZkddV5Ct1BSX1d2ClQgnAEJEQEFMrCur0MJX96vCV+tvy3V31ILxfTSmMNfCnsESjQ3ST7PNbd2ukGa9bU1/ANgeUzyIunVlFZq5apcpnEiS11ermat2aV1ZhUU9gyUOvB4YTmZsIJwAaBUjKOdhSiJyDY2GFr66X8FOoDQkuSQtfHW/RvXxcG+TQbApnQUnJBf/7QG0joDyBaYkouPt8uMBIyfnMyRV+Gr1dvlxDb60a/w6hvhjvQmACDDFI6YkoqmypuVw0p7r4FCEEwARSvqA0taUhHR2SqKhMdgVaK5HZseoXgcHIpwAiIKkDyjhTEmgbdcVZCvX3VEtrTBw6ezU2XUF2S1cAcf6eH9gOLnxx4QTAO2S9GtQmJKIrtQUlxaM76OZq3bJJZlGpppCy4LxfVggm2j+o7907F1z2/yPpIwLrekPAMdL+hEUpiSib0xhrlbcfo08bvM987g7asXt17DoONEUuwPDSbGPcAIgIkk/gtI0JeH11QZdh+LS2Q9WpiTCM6YwV6P6eNi2nehYbwIgRpI+oDAlETupKS62EicywgmAGEr6KR6JKQmc1dBo6K33junl3R/prfeOsXOrJfWnAsNJZi7hBEBUJf0IShOmJJIbhfpCtPEX0uYl5rY7/yrlXmVNfwAkLJdhGI77M7G6ulput1s+n09ZWVlWdwcO11Sor/n/EJqiKaNoX2BKB0CEwvn8ZooHSY1CfSEinACIMwIKkhqF+kJAOAFgAQIKkhqF+tpAOAFgEQIKkhqF+lpQvjkwnPzLrwgnAOKGXTxIahTqCyLYqMlPPpVS0+LfFwBJixEUJLWmQn2SAg44TMpCfS1N6RBOAMQZAQVJj0J9X2C9CQAbYYoHEIX6CCcA7IaAAnwhKc8OOvmJ9KvLTE2V3YfovTGrdF2jkTwBDYDtEFCAZPXnGVLZn01NQ2p/o6OHu0lPbKPUPwBLsQYFSEbF7oBwcnHtah1VN//3Xl+tZq7apXVlFfHuHQAQUICkE2S9ycW1qwPaKPUPwEoEFDhWQ6Oht947ppd3f6S33jvGh2goQgwnTSj1D8AqrEGBI60rq9DCV/ebztFhzUQrGhuln3YJaH755v1Sye42n560pf4BWIYRFDjOurIKzVy1K+CQP9ZMtOB/nw8MJ9/6o1Tso9Q/ANtiBAWO0tBoaOGr+4OWpTd0tvrrwlf3a1QfD1tkpeD1TRackFxn7w2l/gHYFSMocJS3y48HjJycjzUT52mp+JrrXHCj1D8AuyKgwFFCXQuR9GsmwqgMS6l/AHbEFA8chTUTIWhH2fqkL/UPwHYIKHAU1ky0ovLv0uODzG19b5NufSKkpydlqX8AtkVAgaM0rZmYuWqXXJIppCT1molfXSGd9Jrb7iuXLkjCoAYgIbAGBY4zpjBXv516jbp0Tje123XNRMwLyhW7A8NJsY9wAsDRGEGB46wrq9DPXtuv46fq/W3ZndP0k3/9iu3CScwLyrVjvYkTNDQarIcBklzYIyibN2/W+PHjlZeXJ5fLpZdeesn0uGEYKi4uVl5enjp16qRhw4Zp3759pmvq6uo0e/ZsdevWTZ07d9aECRN05MiRiF4IkkNLRdqqTp3RPavfsVWRtpgXlEvQcLKurEJDH9moKU9s070luzXliW0a+shGW/23BRB7YQeUU6dOqV+/flq+fHnQx5csWaKlS5dq+fLl2r59uzwej0aNGqWamhr/NUVFRVqzZo1KSkq0ZcsWnTx5UuPGjVNDQ0P7XwkSXltF2iT7HGwX076eOZ3Q4YQqwQCkdgSUsWPH6uc//7kmTZoU8JhhGPr1r3+tBx98UJMmTVJhYaGefvppffbZZ1q9+uyBZD6fT08++aQeffRRjRw5Ul/72te0atUq7d27Vxs2bIj8FSFhOalIW8z6uqFY+oXH3Pbd1xMinDgpgAKIvaguki0vL5fX69Xo0aP9bRkZGbrhhhu0detWSdLOnTt15swZ0zV5eXkqLCz0X9NcXV2dqqurTV9IPk4q0haTvha7pS3LmrX5pF5DwuiZfTkpgAKIvagGFK/37E6CnJwcU3tOTo7/Ma/Xq/T0dHXp0qXFa5pbvHix3G63/ys/Pz+a3YZDOKlIW9T7mqBTOudzUgAFEHsx2WbscplX2xuGEdDWXGvXzJ8/Xz6fz/91+PDhqPUVztFUpK2ld5JLZ3fI2KFIW1T7GuNw0nwbdP3njbHdFt0CJwVQALEX1W3GHs/ZuXGv16vc3HNbKCsrK/2jKh6PR/X19aqqqjKNolRWVmrIkOBD1RkZGcrIyIhmV+FATirSFrW+xjicBNsGneKSzs8kUd0W3QqqBAM4X1RHUAoKCuTxeLR+/Xp/W319vUpLS/3ho3///kpLSzNdU1FRobKyshYDCtDESQfbRdTXg+sDw8ngWVEPJ8F2zDQfMInXDhpOVgZwvrBHUE6ePKl3333X/315ebl2796t7OxsffnLX1ZRUZEWLVqk3r17q3fv3lq0aJEuuOACTZ06VZLkdrs1Y8YMzZ07V127dlV2drbmzZunvn37auTIkdF7ZUhYTjrYLty+NjQaSv3plwIfePBjKS16Uxut7ZhpztDZgLDw1f0a1ccT0/vcFOqaj+p44jSKA8A+wg4oO3bs0I033uj/fs6cOZKkadOmaeXKlbrvvvt0+vRp3X333aqqqtLAgQP1xhtvKDMz0/+cZcuWqUOHDrrtttt0+vRpjRgxQitXrlRqamoUXhKSgZMOtgu1r+vKKjTmz1cGtn/zHxoTxXAitb1jprnzd9DE+r47KYACiB2XYRiOKypQXV0tt9stn8+nrKwsq7sDRKylcFJQe7Z+ULSnr17e/ZHuLdkd9vMe+9bVmnj1RVHrB4DkEs7nN4cFAhZraDSChpOLa1fHrEBZe3fCsIMGQLwQUAArVVcEXXNy8RcjJ1JsCpS1tQ26OTtt4QaQHAgogFV+N1Raah45GVP3sCmcnC+aBcpa2zHTHDtoAFiBgAJYodgtefeami6uXa1/GF9u8SnRnl5paRt08wxixy3cABJfVAu1AQhBkOJrgzNelKs2/gXKgu2Y6d+ri3Z+UMUOGgCWIqAA8dRCZdgFXxRNs6JCbrBt0E7Zwg0gcTHFg4TS/FyZeJ0j06bGxlbL1jupQi4AxAMjKEgYwc6Vidc5Mq1663HpL/PNbTctkgbfY2qiQBkAnEOhtiTS0Ggk7Idf07kyzd/MTa/OslGIYKMmD1VJKQxeAkg+4Xx+M4KSJGw7uhAFrZ0rE89zZALE+CRiAEhk/BmXBFo6tTZep9TGWlvnysSi0FmbCCcAEBECSoJra3RBin4Z9XgLtYBZNAudtYpwAgARI6AkOFuOLkRZqAXMYn6OzMf7A8NJr6GEEwBoB9agJDjbjS7EQNO5Ml5f/Aud+QUbNZn7TykzJ3a/EwASGCMoCc42owsx1Nq5MtEqdNZqfZWWpnQIJwDQboygJDhbjC7EQVOhs+Y7lTxR2KnU6g6oP18Z+ASmdAAgYtRBSQJNu3ik4GXUE6lSabRrvbRWX6W849TAJxBOAKBF1EGBSSxHF+wm2Lky7dXSDqgM1etAx+mBTyCcAEDUEFCSBGXUwxdsB9TPOvynvt1hg6ntHzf8TlfeOCWeXQOAhEdASSLRHF1IBs13Nh0KMqVzce1qPdblagVZiQIAiAABBWjB+TubWgonza8LVSKfiwQA0UBAAVrQtAPqrbpJAY9dXLu63TugEvlcJACIFuqgAC1ITXG1Gk6k8OurtHQuUoWvVnet2qW1e45G0mUASBgEFCCYdzcEFGB7sWGof1rH4+4Y9vbs1s5FajLrj+9o7Z7oHN7YanE5ALA5pniA5oJUhm340RHlflSvxyJYM9LWuUiS1GhId6/epd+lRFabhmkkAE7HCApwvhbK1qd2zNTgS7tq4tUXafClXdu1oDWc844iOWG6pWkkr69WM1ft0rqy6IzQAEAsEVCAJi2dqRMl4ez2ae8J061NIzW1RRJ+ACBeCCiAFPNwIp3bFRSq9pww3dY0kqH2hx8AiCcCCpLbqU/jEk4k86nLoWhPfZVQQ017wg8AxBMBBcnrP8dIv7zU3HbHxpieqTOmMFePT/2aWlvC4tLZBa3tOWE61FDTnvADAPHELh4kp3aOmkSjAuy/XJWn5XLp7tW7Ah5rb32VJk3TSF5fbdB1KO0tLgcA8UZAQfJpZziJ5tbdf7kqV79Lif4J003TSDNX7ZJLMoWUSMMPAMSTyzAMxy3nr66ultvtls/nU1ZWltXdgZNEEE5mrtoVMCrR9DEfbtG2JrE6k4c6KADsKJzPb0ZQkBwMQ1r4pcD2EKd12tq6+8CavTp9plGerPBCRqxOmB5TmKtRfTwcSAjAsQgoSChBRyR2/EFaO8984fAfS9f/MKSfGUoF2OOnzugHz++WZJ+RiliFn3ji1GcgeRFQkDCCTWsc6jg18MKHjkspqSH/3HC35DZVbG3vtA/OYpoKSG5sM0ZCCFbePWg4KfaFFU6k8LfkJnrF1ngcQki5fgCMoMDxgq0RCRZOGh46ofCiyVnXFWQru3Oajp86E/Jzzq/Y6vRplvPFY1SjrTU/Lp0Nf6P6eJjuARIYIyhwvOZrRIKFk4trV7e7vHtqiku3XH1Ru56bSBVb4zWqQbl+ABIBBQmgKQT0dH0SEE62N16ui2tXm65rj5F9PO16XqJUbI3nIYSU6wcgMcWDBNAjs6NWpC3T2NTtpvZrax/XJ/qS6br2aqtCa3OJVrE1nFGNSKe0KNcPQGIEBQlg8LOXBISTi2tX+8NJJGfbNDn/oL+2Vj0kYsXWeI5qNIXBlu5cNP57ArA/AgqcLUhl2KYpHSm8sNDW7pQxhblacfs18rjNf7k3/7Eed8eE22Icz1GN1sJgIoY/AMExxQPnChJOLqlbbfre5ZLu+EZBm2Eh1N0pwSq09u/VRTs/qEroYmLxPoSwKQxG+6wiAM7BWTxwnoYz0s+6mZoaXR106elnWvzwbG1EI1bn7CSapvskBT+EMBb3iUqyQGIJ5/ObKR44y65nAsJJw3f+S19P/1Ori1db2mESz90pTtfSFFcsp7SayvVPvPoiDb60K+EESCJM8cA5WjiJ+O33jqnCt63Fp7W2wySS3SnJ+Nc9hxACiBcCCpyhhXAiRbbDpL3PTeZzYhLhEEIA9hf1KZ7i4mK5XC7Tl8dzrsiVYRgqLi5WXl6eOnXqpGHDhmnfvn3R7gYSSSvhRIpsh0l7nss5MQAQezFZg/LVr35VFRUV/q+9e/f6H1uyZImWLl2q5cuXa/v27fJ4PBo1apRqampi0RU4XRvhRIqsbka4z21rzYohqfiVfaxZAYAIxSSgdOjQQR6Px//VvXt3SWdHT37961/rwQcf1KRJk1RYWKinn35an332mVavXt3GT0VSOV4eGE7GLgkIJ1LbdTMMSd+6Nl//tedoQH2TcGtutLVmRZK81XVavvHdVq+xu3icWGwHyfI6ASeKyRqUgwcPKi8vTxkZGRo4cKAWLVqkSy65ROXl5fJ6vRo9erT/2oyMDN1www3aunWr7rzzzqA/r66uTnV1df7vq6urY9Ft2MULd0h7/2Rue6BCSr+gxae0VDfDfUGaJGnZhoP+tuZrRcKpuRHqmpVlG/6pKzwXOnI9SrKsr0mW1wk4VdTroLz++uv67LPPdPnll+vjjz/Wz3/+c/3jH//Qvn37dODAAX3961/XRx99pLy8PP9zvv/97+uDDz7QX/7yl6A/s7i4WAsXLgxopw5KAgphSqc15++sOfTpZ/r1hn+GXN8klF05b713TFOeaHnH0Ply3R215f7hjtrhkiw1YZLldQJ2Y2kdlLFjx+rWW29V3759NXLkSL322muSpKefftp/jctl/j9swzAC2s43f/58+Xw+/9fhw4ej3W3YQYThRDq3w2TcVXkq2f5hWPVNQqm50bRmJRRN25OdIllqwiTL6wScLuaF2jp37qy+ffvq4MGD/t08Xq/XdE1lZaVycnJa/BkZGRnKysoyfSHBRCGcnC+c+ibhOH/NSiiicXhevMTqntlNsrxOwOliHlDq6ur097//Xbm5uSooKJDH49H69ev9j9fX16u0tFRDhgyJdVdgR2dOB4aTq2+PKJxIsT19d0xhrn4wsndI10bj8Lx4ieeJxVZKltcJOF3UA8q8efNUWlqq8vJy/e1vf9M3v/lNVVdXa9q0aXK5XCoqKtKiRYu0Zs0alZWVafr06brgggs0derUaHcFdrfnT9IvPOa2e/dIN/824h8d69N3Zw3vLU9W68/N7pwmb3WtY3aHxPPEYisly+sEnC7qu3iOHDmiKVOm6NNPP1X37t01aNAgbdu2Tb169ZIk3XfffTp9+rTuvvtuVVVVaeDAgXrjjTeUmZkZ7a7AQm0uOI3ylE5zsT59NzXFpeIJfYIentfk+Kkz+sHzuyU5Y3dIvE8stkqyvE7A6TjNGFHX5vbNGIeT8/sR69N3g73WYJyyO8SKE4utkCyvE7CbcD6/CSiIqra2b5Z3DDKVF4Nwcn5/Yl3romm0yOs7rZ+99ncdP1Uf9Lqmv8ztvvU4WeqDJMvrBOyEgOJgTj4ht6HR0NBHNrYwmmDoUMf/Y27KvkT693fi0q943NNQa6T88Y5Btj9sz8nvw3Aky+sE7CKcz29OM7YRp/9F19L2zUtcR7UxY5658TsvS5cMi0u/4nX6rl12h0TjQzdRTywOdm8S8XUCiYCAYhMtTY00nZDrhDnxYB+8P+/wpG7v8N+mtpcn7tXES74cr27FjR12hzg95MYS9wZwlpjXQUHbEqWyZfMP3kMdpwaEk4trV6tHVueIfo9dD3iL5FTlaGgKuc1HsZpC7rqyipj8Xifg3gDOQ0CxgUSpbHn+B/ShIIthC2pXR/wBva6sQkMf2agpT2zTvSW7NeWJbRr6yEZbfMCEezJyNCVKyI0F7g3gTAQUG7DL2oVINX1AB9upU1C7WlJkH9BO+Cu46WRkT7PzejzujjGdpkuUkBsL3BvAmViDYgN2WLsQFWdOa8yfrzQ13VE/R+sbB0Q819/WX8Eunf0reFQfj+W7MMYU5mpUH09cd4ckSsiNBe4N4EwEFBtIiMqWnx6Ulg8wNf1tyj6NO+3S96LwAR3OX8F22JUR710wCRNyY4B7AzgTUzw2YOXahajY+XRAOFGxTwOv6KmJV1+kwZd2jbjv/BXcOqsX6NoZ9wZwJgKKTVi1diFiT/2r9Oq/m9tiUBmWv4Jb5/iQG0PcG8CZqCRrM46qbNn8TJ2v3yuN+mlMflVTldq2psHsXkY+1qj10TLuDWA9St0jtgxDWvglc9v0tdLFX4/pr+WAt9A4KuTGGfcGsBYBBbFz8hPpV5eZ2+4/JHXqEpdfz1/BAOBcnMWD2Di4QXruVnPbghOSK35/gYa6hZe/lAHA2QgoCM1//UDa8Z/mthgshg1FW1t4GWUBAOdjFw/aVuw2h5Mr/tWycNIWJ1SbBQC0jYCC1jXfqXPrk9KU1db0pQ2cuQIAiYOAguAazgSGk3v3SH2/aU1/QsCZKwCQOFiDgkA1H0uPXm5ue+i4lJJqTX9CRLVZAEgcjKDA7PDb5nBy5biz601sHk4kqs0CQCIhoOCct5+Qnhx17vsxD0vfes66/oSJM1cAIHEQUHDW/5surZ137vvvvi4NmmlZd9qDM1cAIHEQUJKdYUg/90j71pxrm/MPqdcQ6/oUAcceuggAMGGRbDKrrZYezje3/fgTqUO6Nf2JklCrzQIA7IuAkqwq/yE9PvDc956rpLv+al1/oqytarMAAHsjoCSJ88+mufL4Rl2xeda5B4f+QBpZbFnfAABojoCSBM4/m+bHHZ7VxA6vn3vwW6ulK//Vus4BABAEASXBNZ1NY0han/5D9U75yP/Y8LpHdd/n12iMdd0DACAodvEksKazadJ0Roc6TjWFkz61/6lyI5ezaQAAtkRASWBvlx/Xp76T+mfHaf62KuNCXVz7nD5TR86mAQDYFgElgVXW1GpAygH/9y80DNXX6n6v5mXMOJsGAGA3rEFJYD0yO2pH4xWaf2aG3m28SNuNK1u8DgAAOyGgJLDrCrLVzX2hSnwjFGyViUtnK6xyNg0AwG6Y4klgnE0DAHAqAkqC42waAIATMcWTBDibBgDgNASUJMHZNAAAJ2GKBwAA2A4BBQAA2A4BBQAA2A4BBQAA2A4BBQAA2A4BBQAA2A4BBQAA2A4BBQAA2A4BBQAA2I4jK8kaxtmzeaurqy3uCQAACFXT53bT53hrHBlQampqJEn5+fkW9wQAAISrpqZGbre71WtcRigxxmYaGxt19OhRZWZmyuVy/oF31dXVys/P1+HDh5WVlWV1d2yBexIc9yUQ9yQ47ksg7kmgeN8TwzBUU1OjvLw8paS0vsrEkSMoKSkp6tmzp9XdiLqsrCz+R9MM9yQ47ksg7klw3JdA3JNA8bwnbY2cNGGRLAAAsB0CCgAAsB0Cig1kZGRowYIFysjIsLortsE9CY77Eoh7Ehz3JRD3JJCd74kjF8kCAIDExggKAACwHQIKAACwHQIKAACwHQIKAACwHQKKDTz++OMqKChQx44d1b9/f/31r3+1uktxU1xcLJfLZfryeDz+xw3DUHFxsfLy8tSpUycNGzZM+/bts7DH0bd582aNHz9eeXl5crlceumll0yPh3IP6urqNHv2bHXr1k2dO3fWhAkTdOTIkTi+iuhq655Mnz494H0zaNAg0zWJdk8WL16sa6+9VpmZmerRo4duvvlmHThwwHRNMr5XQrkvyfZ+WbFiha666ip/8bXBgwfr9ddf9z/ulPcJAcVizz//vIqKivTggw/qnXfe0Te+8Q2NHTtWH374odVdi5uvfvWrqqio8H/t3bvX/9iSJUu0dOlSLV++XNu3b5fH49GoUaP85zElglOnTqlfv35avnx50MdDuQdFRUVas2aNSkpKtGXLFp08eVLjxo1TQ0NDvF5GVLV1TyRpzJgxpvfN2rVrTY8n2j0pLS3VPffco23btmn9+vX6/PPPNXr0aJ06dcp/TTK+V0K5L1JyvV969uyphx9+WDt27NCOHTs0fPhwTZw40R9CHPM+MWCp6667zrjrrrtMbVdeeaXxox/9yKIexdeCBQuMfv36BX2ssbHR8Hg8xsMPP+xvq62tNdxut/G73/0uTj2ML0nGmjVr/N+Hcg9OnDhhpKWlGSUlJf5rPvroIyMlJcVYt25d3PoeK83viWEYxrRp04yJEye2+JxEvyeGYRiVlZWGJKO0tNQwDN4rTZrfF8Pg/WIYhtGlSxfjD3/4g6PeJ4ygWKi+vl47d+7U6NGjTe2jR4/W1q1bLepV/B08eFB5eXkqKCjQt771Lb3//vuSpPLycnm9XtP9ycjI0A033JA09yeUe7Bz506dOXPGdE1eXp4KCwsT+j5t2rRJPXr00OWXX6477rhDlZWV/seS4Z74fD5JUnZ2tiTeK02a35cmyfp+aWhoUElJiU6dOqXBgwc76n1CQLHQp59+qoaGBuXk5Jjac3Jy5PV6LepVfA0cOFDPPPOM/vKXv+iJJ56Q1+vVkCFDdOzYMf89SOb7E8o98Hq9Sk9PV5cuXVq8JtGMHTtWzz33nDZu3KhHH31U27dv1/Dhw1VXVycp8e+JYRiaM2eOhg4dqsLCQkm8V6Tg90VKzvfL3r17deGFFyojI0N33XWX1qxZoz59+jjqfeLI04wTjcvlMn1vGEZAW6IaO3as/999+/bV4MGDdemll+rpp5/2L2JL5vvTpD33IJHv0+TJk/3/Liws1IABA9SrVy+99tprmjRpUovPS5R7MmvWLO3Zs0dbtmwJeCyZ3yst3ZdkfL9cccUV2r17t06cOKEXXnhB06ZNU2lpqf9xJ7xPGEGxULdu3ZSamhqQSCsrKwPSbbLo3Lmz+vbtq4MHD/p38yTz/QnlHng8HtXX16uqqqrFaxJdbm6uevXqpYMHD0pK7Hsye/ZsvfLKK3rzzTfVs2dPf3uyv1daui/BJMP7JT09XZdddpkGDBigxYsXq1+/fnrssccc9T4hoFgoPT1d/fv31/r1603t69ev15AhQyzqlbXq6ur097//Xbm5uSooKJDH4zHdn/r6epWWlibN/QnlHvTv319paWmmayoqKlRWVpY09+nYsWM6fPiwcnNzJSXmPTEMQ7NmzdKLL76ojRs3qqCgwPR4sr5X2rovwSTD+6U5wzBUV1fnrPdJ3JbjIqiSkhIjLS3NePLJJ439+/cbRUVFRufOnY1Dhw5Z3bW4mDt3rrFp0ybj/fffN7Zt22aMGzfOyMzM9L/+hx9+2HC73caLL75o7N2715gyZYqRm5trVFdXW9zz6KmpqTHeeecd45133jEkGUuXLjXeeecd44MPPjAMI7R7cNdddxk9e/Y0NmzYYOzatcsYPny40a9fP+Pzzz+36mVFpLV7UlNTY8ydO9fYunWrUV5ebrz55pvG4MGDjYsuuiih78nMmTMNt9ttbNq0yaioqPB/ffbZZ/5rkvG90tZ9Scb3y/z5843Nmzcb5eXlxp49e4wHHnjASElJMd544w3DMJzzPiGg2MBvf/tbo1evXkZ6erpxzTXXmLbHJbrJkycbubm5RlpampGXl2dMmjTJ2Ldvn//xxsZGY8GCBYbH4zEyMjKM66+/3ti7d6+FPY6+N99805AU8DVt2jTDMEK7B6dPnzZmzZplZGdnG506dTLGjRtnfPjhhxa8muho7Z589tlnxujRo43u3bsbaWlpxpe//GVj2rRpAa830e5JsPshyXjqqaf81yTje6Wt+5KM75fvfe97/s+U7t27GyNGjPCHE8NwzvvEZRiGEb/xGgAAgLaxBgUAANgOAQUAANgOAQUAANgOAQUAANgOAQUAANgOAQUAANgOAQUAANgOAQUAANgOAQUAANgOAQUAANgOAQUAANgOAQUAANjO/w+JQQiuyce7RwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(y_hat_merf_lm, y_test, \"o\")\n",
+    "plt.plot(y_test, y_test, \"-\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b985593e-11c8-48c8-8c09-6f59d4a3e872",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1729.2322117100189 at iteration 1.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1722.6336802748651 at iteration 2.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1741.7049929706577 at iteration 3.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1762.391802675483 at iteration 4.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1757.2373504751026 at iteration 5.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1829.2879109190922 at iteration 6.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1843.3488100314746 at iteration 7.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1845.6852501846533 at iteration 8.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1854.0603037378419 at iteration 9.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1841.5651430986702 at iteration 10.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1840.8167716915584 at iteration 11.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1858.6193161061392 at iteration 12.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1855.8811580852757 at iteration 13.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1877.5603737348674 at iteration 14.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1888.8149421934836 at iteration 15.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
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+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1898.6859052262948 at iteration 16.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1901.1670785781039 at iteration 17.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1909.0686819364796 at iteration 18.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1920.060517314308 at iteration 19.\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [_binning.py:2584] Creating native dataset\n",
+      "INFO     [ebm.py:702] Estimating with FAST\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [bin.py:29] eval_terms\n",
+      "INFO     [merf.py:307] Training GLL is 1925.9246843298638 at iteration 20.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<merf.merf.MERF at 0x7fdb688b1f90>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrf_ebm = MERF(ebm)\n",
+    "mrf_ebm.fit(X_train, Z_train, clusters_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "3210e5e1-bc3e-4b7c-bae9-a7326a064dda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO     [bin.py:29] eval_terms\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_hat_merf_ebm = mrf_ebm.predict(X_test, Z_test, clusters_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "aa8d71ec-0f0e-4b36-9e7c-ec0688ef4a07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MERF+EBM Regression Model\n",
+      "mean_absolute_error:  29.893672342605583\n",
+      "mean_squared_error:  1805.5081944718838\n",
+      "r2:  0.7881151017126167\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"MERF+EBM Regression Model\")\n",
+    "print(\"mean_absolute_error: \", mean_absolute_error(y_test, y_hat_merf_ebm))\n",
+    "print(\"mean_squared_error: \", mean_squared_error(y_test, y_hat_merf_ebm))\n",
+    "print(\"r2: \", r2_score(y_test, y_hat_merf_ebm))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "d3bf32eb-6e04-476b-aae9-ba8224d72e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fdb689fd4b0>]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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1qqqqdODAgaABBQAAkzKHfzgpcxNOkkhYPSizZ8/WmjVr9Oqrryo7O9s3Z8ThcKhr1646deqUysrKdNdddyk/P19Hjx7Vo48+qp49e+rOO+/01Z05c6bmz5+vHj16KDc3VwsWLNCAAQN8q3oAAAiKJcQpIayAsnz5cknSiBEjTOUrVqzQfffdp/T0dO3fv18vvPCCTp48qfz8fI0cOVIvv/yysrOzffWfffZZdenSRVOnTlVdXZ1Gjx6tlStXKj09vfPvCACQnA69Kv3v75vL8gZIsyoC10dCsxmGYcS7EeHyeDxyOBxyu93KycmJd3MAANEWcAnxR1K3nrFvCzosnO9vzuIBAFgbQzopidOMAQDWVFdDOElh9KAAAKzn+dulY7vMZVOek66fGp/2IOYIKAAAa6HXBGKIBwBgJYQTfI2AAgCIv/c3+IeT3tcRTlIYQzwAgPgK1Guy4EPp4l6xbwssg4ACAIgfhnQQBEM8AIDYYwkx2kEPCgAgtlZ8W/rkr+ayO/9dGvi9+LQHlkRAAQDEDr0mCBFDPACA2CCcIAwEFABAdL3/n/7hpNe1hBO0iSEeAED0sIQYHURAAQBEB0M66ASGeAAAkVV3knCCTqMHBQAQOSv+Qfqkwlx2x2+lG+6OT3uQsAgoAIDIoNcEEcQQDwCg8wgniDACCgCg4/620T+c9LyGcIJOY4gHANAxAZcQH5Eu7h37tiDpEFAAAOFjSAdRxhAPACB09W7CCWKCHhQAQGhWTZQqd5jL7lgu3TA9Pu1BUiOgAADaR68JYowhHgBA2wgniAMCCgAgsMNv+IeTHv0IJ4gJhngAAP4C9ZrM/0DKzot9W5CSCCgAADOGdGABDPEAAFqwhBgWQg8KAEBaNUmqLDeXTf6N9I1/jE97kPIIKACQ6ug1gQUxxAMAqYxwAosioABAKjq8yT+c5F5JOIFlMMQDAKmGJcRIAAQUAEglDOkgQTDEAwCpoN5DOEFCoQcFAJLdi1Okj/6vuWzyr6Vv3BOf9gAhIKAAQDKj1wQJiiEeAEhWhBMkMAIKACSbD/7sH066FxFOkFAY4gGAZBJwCfFhKdsZ+7YAnUBAAYBkwZAOkghDPACQ6FhCjCREDwoAJLLVd0kfbjWXTfo36cbvx6c9QIQQUAAgUdFrgiTGEA8AJCLCCZJcWAFl6dKluvnmm5Wdna3evXvrjjvu0OHDh011DMNQWVmZCgoK1LVrV40YMUIHDx401WloaNDcuXPVs2dPdevWTZMmTdLx48c7/24AINl9sNk/nFxyGeEESSesgFJeXq7Zs2dr165d2rJli86dO6eSkhKdPn3aV+epp57SM888o2XLlmn37t1yOp0aO3asamtrfXVKS0u1fv16rV27VhUVFTp16pQmTJigpqamyL0zAEg2ZQ5pzXfNZfP+JpXuj097gCiyGYZhdPSHv/jiC/Xu3Vvl5eW67bbbZBiGCgoKVFpaqkceeURSS29JXl6ennzyST3wwANyu93q1auXXnzxRU2bNk2S9Nlnn6mwsFAbN27U7bff3u7rejweORwOud1u5eTkdLT5AJA4GNJBEgjn+7tTc1Dc7pb/OXJzcyVJlZWVcrlcKikp8dWx2+0aPny4du7cKUnas2ePzp49a6pTUFCg4uJiX53WGhoa5PF4TDcASAkNtYQTpKQOBxTDMDRv3jzdeuutKi4uliS5XC5JUl5enqluXl6e7zGXy6XMzEx17949aJ3Wli5dKofD4bsVFhZ2tNkAkDhWf0da2sdcNvF/EU6QEjq8zHjOnDl67733VFFR4feYzWYz3TcMw6+stbbqLFy4UPPmzfPd93g8hBQAyS1Qr8mik1I7/5YCyaJDPShz587Va6+9pjfffFN9+pxP905ny1kPrXtCqqurfb0qTqdTjY2NqqmpCVqnNbvdrpycHNMNAJJWsCEdwglSSFgBxTAMzZkzR+vWrdO2bdtUVFRkeryoqEhOp1NbtmzxlTU2Nqq8vFzDhg2TJA0aNEgZGRmmOlVVVTpw4ICvDgCkpCNb/cOJo5AhHaSksIZ4Zs+erTVr1ujVV19Vdna2r6fE4XCoa9eustlsKi0t1ZIlS9SvXz/169dPS5Ys0UUXXaTp06f76s6cOVPz589Xjx49lJubqwULFmjAgAEaM2ZM5N8hACSCQL0m8/4m5eTHvi2ABYQVUJYvXy5JGjFihKl8xYoVuu+++yRJDz/8sOrq6vTQQw+ppqZGgwcP1ubNm5Wdne2r/+yzz6pLly6aOnWq6urqNHr0aK1cuVLp6emdezcAkIhYpQP46dQ+KPHCPiiIpqZmQ29XfqXq2nr1zs7SLUW5Sk9j7B9R0FDrv0pHIpwgaYXz/c1hgcAFNh2o0uINh1TlrveV5TuytGhif40rpqsdEfTSd6Ujm81lE34p3fTf49IcwGoIKMDXNh2o0qzVe9W6S9Hlrtes1Xu1/J4bCSmIDJYQA+3iNGNALcM6izcc8gsnknxlizccUlNzwo2IwmpYQgyEhIACSHq78ivTsE5rhqQqd73ervwqdo1Ccjn4in84ybmU+SZAEAzxAJKqa4OHk47UA0wCLiF+X8opiH1bgARBQAEk9c7Oimg9wIclxECHMMQDSLqlKFf5jiwFmwVgU8tqnluKcmPZLCSyeg/hBOgEAgogKT3NpkUT+0uSX0jx3l80sT/7oSA0v71VeqLVgaajf0o4AcJAQAG+Nq44X8vvuVFOh3kYx+nIYokxQlfmkFz7zWWLTkrfmh+X5gCJijkowAXGFedrbH8nO8miYxjSASKGgAK0kp5m09Are8S7GUgkh16T/ve9/uWEE6DDCCgA0BmBek1++F9S98tj3hQgmRBQAKCjGNIBooZJsgAQLpYQA1FHDwoAhOO335Jc75nLRv1Euu1H8WkPkKQIKAAQKk4hBmKGgAIAoWBIB4gp5qAAQFve30A4AeKAHhQACCZQMPkf+6Tcopg3BUg1BBQACIReEyCuGOIBgAs11BJOAAugBwUAvH43Uvpsr7ls5E+k4SwhBmKNgAIAEkuIAYshoAAAQzqA5TAHBUDq+ttGwglgUfSgAEhNAZcQvyvlXhH7tgDwQ0ABkHroNQEsjyEeAKmj4RThBEgQ9KAASA3PjZI+3WMuG/mYNPzh+LQHQJsIKACSH0uIgYRDQAGQ3BjSARISc1AAJKfDbxBOgARGDwqA5MMSYiDhEVAAJBd6TYCkwBAPgOTAEmIgqdCDAiDx/X6sdPxtc9mIhdKIH8enPQA6jYACILGxhBhISgQUAImLIR0gaTEHBUDi+eDPhBMgydGDAiCxBAomc/dKPa6MfVsARA0BBUDioNcESBkM8QCwPpYQAymHHhQA1vYf46S/v2UuG/6INPLR+LQHQEwQUABYF0uIgZRFQAFgTQzpACmNOSgArIUlxABEDwoAK2EJMYCvEVAAWAO9JgAuEPYQz44dOzRx4kQVFBTIZrPplVdeMT1+3333yWazmW5Dhgwx1WloaNDcuXPVs2dPdevWTZMmTdLx48c79UYAJKjG04QTAH7CDiinT5/WwIEDtWzZsqB1xo0bp6qqKt9t48aNpsdLS0u1fv16rV27VhUVFTp16pQmTJigpqam8N8BgMS14tvSkgJz2W0/IpwACH+IZ/z48Ro/fnybdex2u5xOZ8DH3G63nn/+eb344osaM2aMJGn16tUqLCzU1q1bdfvtt4fbJACJiCXEANoQlVU827dvV+/evXX11VfrBz/4gaqrq32P7dmzR2fPnlVJSYmvrKCgQMXFxdq5c2fA52toaJDH4zHdACSwYEM6hBMAX4t4QBk/frxeeuklbdu2TU8//bR2796tUaNGqaGhQZLkcrmUmZmp7t27m34uLy9PLpcr4HMuXbpUDofDdyssLIx0swHEwgebmW8CICQRX8Uzbdo0338XFxfrpptuUt++ffX6669rypQpQX/OMAzZgvz1tHDhQs2bN8933+PxEFKARBMomMzZI/W8KvZtAWB5UV9mnJ+fr759++rIkSOSJKfTqcbGRtXU1Jh6UaqrqzVs2LCAz2G322W326PdVADRQq8JgDBFfSfZEydO6NixY8rPz5ckDRo0SBkZGdqyZYuvTlVVlQ4cOBA0oABIUCwhBtBBYfegnDp1Sh9++KHvfmVlpfbt26fc3Fzl5uaqrKxMd911l/Lz83X06FE9+uij6tmzp+68805JksPh0MyZMzV//nz16NFDubm5WrBggQYMGOBb1QMgCaz4B+mTCnPZt+ZLo38an/YASChhB5R33nlHI0eO9N33zg2ZMWOGli9frv379+uFF17QyZMnlZ+fr5EjR+rll19Wdna272eeffZZdenSRVOnTlVdXZ1Gjx6tlStXKj09PQJvCUDcsYQYQCfZDMMw4t2IcHk8HjkcDrndbuXk5MS7OQAuxJAOgCDC+f7mNGMAkXFkC+EEQMRwWCCAzgu4hPgdqWe/2LcFQFIgoADoHHpNAEQBQzwAOqbxDOEEQNTQgwIgfCsnSEf/Yi77Zqk0dnFcmgMg+RBQAISHJcQAYoCAAiB0DOkAiBHmoABo34dbCScAYooeFABtCxRMZu+Wel0d+7YASBkElBTW1Gzo7cqvVF1br97ZWbqlKFfpacwjwAXoNQEQJwSUFLXpQJUWbzikKne9ryzfkaVFE/trXHF+HFsGS2g8Iy0J8DkgnACIEeagpKBNB6o0a/VeUziRJJe7XrNW79WmA1Vxahks4YXJ/uHkmz8knACIKXpQUkxTs6HFGw4p0AmRhiSbpMUbDmlsf6dlh3sYmoqiQEM6P62R0vhbBkBsEVBSzNuVX/n1nFzIkFTlrtfblV9p6JU9YtewEDE0FUXMNwFgIfxZlGKqa4OHk47UiyWGpqLk/f8knACwHHpQUkzv7KyI1ouVZBiasqRAweTBCsk5IPZtAYAL0IOSYm4pylW+I0vBvsJtahkyuaUoN5bNalc4Q1MIUbBeE8IJAAsgoKSY9DSbFk3sL0l+IcV7f9HE/pbrhUjkoSnLaTjFkA4AyyOgpKBxxflafs+NcjrMwzhOR5aW33OjJSebJurQlOU8cZm09FJz2XVTCCcALIc5KClqXHG+xvZ3JsxyXe/QlMtdH3Aeik0tActqQ1OWwhJiAAmEgJLC0tNsllxKHIh3aGrW6r2ySaaQYuWhKctgSAdAguFPJ8RcU7Ohtz46oVf3faq3PjqhpuZAfSL+EnFoKu72/5FwAiAh0YOCmOrsRmuJNjQVV4GCyT9tk/oMin1bACBMNsMwQvvz1UI8Ho8cDofcbrdycnLi3RyEyLvRWusPnDda0AsSQfSaALCgcL6/GeJBTLS30ZrUstFaqMM9CKLxNOEEQFIgoCAm2GgtBp66QlpSYC7rdzvhBEBCYg4KYoKN1qKMJcQAkgwBBTHBRmtRxJAOgCTEn1eIiUQ9A8jSDq4nnABIWgQUxESingFkWWUO6f/cZy6buZVwAiBpEFAQM2y0FiHBek0Kb459WwAgSpiDgphio7VOaDwjLQkQ4ug1AZCECCiIuUQ6A6gtTc1G7ILWL66STn9hLrtqrHTPH6PzegAQZwQUoAM6u2V/WAIuIf5KSkuP7OsAgIUwBwUIk3fL/tYbz7nc9Zq1eq82HaiK3IsFm29COAGQ5AgoQBhitmX/wVdYQgwgpTHEA0uJ6byODghny/4Oz7MJFExmbpEKb+nY8wFAAiKgwDJiOq+jg6K+ZT+9JgAgiSEeWERM53V0QtS27G88QzgBgAsQUBB3MZvXEQFR2bL/ycv99ze5cjThBEBKI6Ag7sKZ1xFvEd+yv8wh1dWYy376lXTvuk61EwASHQEFcRf1eR0RFrEt+1lCDABBMUkWcRe1eR1R1Kkt+//rZWn9P/uXM6QDAD4EFMSdd16Hy10fcB6KTS29E2HN64iBDm3ZH6jXZMYGqei2yDQKAJIEQzyIu4jP67CqYEM6hBMA8ENASWBNzYbe+uiEXt33qd766IQlVrl0VMTmdcRYSL+DxtMsIQaAMDHEk6ASYVOzcHVqXkcchPQ7WNxdMprNP5hXLM36a9TaZfXdeAEgFGH3oOzYsUMTJ05UQUGBbDabXnnlFdPjhmGorKxMBQUF6tq1q0aMGKGDBw+a6jQ0NGju3Lnq2bOnunXrpkmTJun48eOdeiOpJFE2NesI77yOyTdcqqFX9rDsF2tIv4Myh384+elXUQ0nmw5U6dYnt+nu53bph2v36e7ndunWJ7cl9GcCQGoKO6CcPn1aAwcO1LJlywI+/tRTT+mZZ57RsmXLtHv3bjmdTo0dO1a1tbW+OqWlpVq/fr3Wrl2riooKnTp1ShMmTFBTU1PH30mKSKRNzZJVKL+DcX+81v/BKC8hTubgCiD1hB1Qxo8fr8cff1xTpkzxe8wwDP3yl7/UY489pilTpqi4uFirVq3SmTNntGbNGkmS2+3W888/r6efflpjxozRN77xDa1evVr79+/X1q1bO/+OklwibWqWrNr6HUxOq1Bl1nT/B6I834TgCiDZRHSSbGVlpVwul0pKSnxldrtdw4cP186dOyVJe/bs0dmzZ011CgoKVFxc7KvTWkNDgzwej+mWqhJtU7NkFOzaHs2arl9l/sZcOGNDTCbDElwBJJuIBhSXyyVJysvLM5Xn5eX5HnO5XMrMzFT37t2D1mlt6dKlcjgcvlthYWEkm51QEnFTs2QT6NoeDdBr8ta9H8dsCTHBFUCyicoyY5vNPLHRMAy/stbaqrNw4UK53W7f7dixYxFra6KJymF1FmeV5dTedrg89crtliFJ6qr6gOFkqH1dTH8HBFcAySaiy4ydTqekll6S/PzzS12rq6t9vSpOp1ONjY2qqakx9aJUV1dr2LBhAZ/XbrfLbrdHsqkJy7up2azVe2WTTHMOkmpTs69ZZTl1oHYECiZfGA7d0rBcy78T299Bou7GCwDBRLQHpaioSE6nU1u2bPGVNTY2qry83Bc+Bg0apIyMDFOdqqoqHThwIGhAgVmibmoWLqusSgnUjkDh5Mr6FzUpa0VcfgcpsxsvgJQRdg/KqVOn9OGHH/ruV1ZWat++fcrNzdVll12m0tJSLVmyRP369VO/fv20ZMkSXXTRRZo+veUfdIfDoZkzZ2r+/Pnq0aOHcnNztWDBAg0YMEBjxoyJ3DtLcom2qVm42luVYlPLqpSx/Z1Rec/ezc5c7jr96+vvm9oRKJysn3RQqx1d4/o78AbX1j09zgTfwA9Aago7oLzzzjsaOXKk7/68efMkSTNmzNDKlSv18MMPq66uTg899JBqamo0ePBgbd68WdnZ2b6fefbZZ9WlSxdNnTpVdXV1Gj16tFauXKn0dI6ZD0eHDqtLEOGsSon0NQg0nCNJ96Zv1r9mrPSrf3n9Gv3B0dUSv4tkD64AUofNMIyE2xjB4/HI4XDI7XYrJycn3s1BFLy671P9cO2+duv96ns3aPINl0bsdb3DOa3/pwjUa3Jf44+0vfkbUWkHACSjcL6/OYsHlhSPVSnBhpUChZPL69dErR0AAE4zhkXFYzl162GlYEuILwwnybisGwCsgIACS4rHqpQLNzE7mjVd72fd71endTiJRjsAAAQUWFisl1N7h2kC9ZpcVf+C37BOsi3rBgArYZIsLM+75Dfaq1Kamg2l/+wSv/ILg0lutwz9y4Tr5MxhdQwAhItJskgqMVlOvfv3Sn99vl+xN5x4Y8iSOwfQYwIAMUBASRGx6oVISGUOv6KFXX6kP5z6hu8+m50BQGwRUFKAVc6zsaQA4URlbj3ebGgSgQ4A4oY5KEku2MZj3q9aq0zyjHkPT+MZaUmA913mjt5rAkCKYw4KJMX/PJtQxbyHJ1CviUQ4AQALYZlxEgvnPJt4ifmJxYHCyb98STgBAIshoCSxCzcei0S9SGuvh0dq6eFpao7QKGSQ+SZKz4jM8wMAIoaAksTicZ5NOGLWw/POiuDhBABgScxBSWLe82xc7vqAvRQ2tSyfjdc5MjHp4QkUTL73B+nab3f8OQEAUUcPShKLx3k24Qi15+bL2oaODfME6zUhnACA5RFQklysz7MJR3snFnv96+vv69Ynt4U+YfZsHUM6AJDg2AclRVh1J1nvKh5JAYehvELet4UlxABgWeF8fxNQEHeB9kEJxDtnpuKRUYHDVaBw8pMvpC6ZkWkoAKBTwvn+ZogHcTeuOF8Vj4zSv/zDf2uzXpureoIN6RBOACAhEVBgCelpNvXMtodU17SqZ+8LzDcBgCTEMmPEnXd+zJHPa0Oq71v9EyiYfOc/pOK7Itg6AEA8EFAQV6HOP5Fa7dtCrwkAJDUCCuIm2EnLgXinxC7+9hVK/9kl/hUiHE6suuoJAFIFAQVx0dY5PIE4HVl6q2GKtD7AgxEOJ6GcrkyAAYDoIqAgLto7h8drzsir9M2remroi1f4PxiFJcTBenW8pysvv+dGSWo3wAAAOoeAgrgI9XydfnkXBw4nUZhv0t7pyjZJP163X+4zZ9sMMIQUAOg8lhkjLkI5h+e76ds1+ZX+/g9EaTJsKKcrnwwQTryPSS09Kx06NwgAYEIPCuKivZOWj2ZN9y+863lpwHei1qZOnZos80ZyQ6/sEZlGAUCKogcFcdHWScsBw0mZO6rhRAr9dOX2dDboAAAIKIij1ict29UYPJzEQKinK7cnUkEHAFIZQzyIqvaW444rztfY/s7Ae5tIMd18zdurM2v1XtlkPl3Ze/+SizICTpL11vFtJAcA6BQCCqImlP1EJAUOJz+plrqEdjZPJHl7dVq32/l1uyUFDTCStGhif/ZDAYAIsBmGkXBLDsI5rhnxEWw/Ee9Xt285rkW3rG+r5yfU4AUAMAvn+5uAkuIivSNqU7OhXR+f0OyX9upk3dmAdWyS/vvFb+mn5/7N/0ELhJNQsJMsAIQvnO9vhnhSWKR6AryhZPWuT7Tjgy90urGpzfqVWdOlc60Kp/xeuv674TQ/rtLTbCwlBoAoIqCkqFC2dA8lpGw6UKUfr9uvk2cC95a0Fs9VOgCAxMEy4xTU3pbuUmg7om46UKUHV+8NKZxk6izhBAAQMnpQUlAoW7q3tyNqU7OhstcOhfR6B+z362Kb/+s1/fSk0kN6BgBAqqEHJQWFutNpW/XervxKLk/7z3M0a7pfOLm6fpWW3vIWk0oBAEERUFJQqDudtlUvlJATaEjn8vo1alSGXvuvKg7VAwAERUBJQe1t6W5Ty2qetnZEbSu8DE07GDSceHmHkAAACISAkoLaOqgv1B1RbynKlTPHP6QczZquP2T+3FR2X+PDpnDixaF6AIBgCCgpqvVBfV5OR1a7S4y9m5R9e4DTVB6s12R78w0Bn4dD9QAAwbCKJ4V5D+oLZ0fUQJu7ddE5fZj1fb+6gXpNJA7VAwC0j4CS4sLZETXQ5m4vZ/5Mg9P+Zqq3u/lqfbexTJI4VA8A0CEEFIQk0OZugYZ0rqp/QefURTZJl1yUIXuXNLk8Db7HnRyqBwAIAQElxXT0kLvWm7u1t0rHkFRz5qxe+qfBSrPZOFQPABAWAkoK6czhgN4VNzfaPtA6e5nf48Hmm3x5qkGTb7i0440GAKSkiK/iKSsrk81mM92czvOrPQzDUFlZmQoKCtS1a1eNGDFCBw8ejHQz0Ip3/kjrLe69hwNuOlDV5s/3zs7S0azpfuHkOw0/DRpOvD8HAEC4orLM+LrrrlNVVZXvtn//ft9jTz31lJ555hktW7ZMu3fvltPp1NixY1VbWxuNpkCRORxw6ItX+JVdXr9G7xjXBv2Z3G4ZGtS3e5itBQAgSgGlS5cucjqdvluvXr0ktfSe/PKXv9Rjjz2mKVOmqLi4WKtWrdKZM2e0Zk3wv8KTRVOzobc+OqFX932qtz46EbOt3sM5HNBP0zmpzOFX3FaviddXp89q+C/ebLd3xu8lo3Cd4nXtAQAdE5U5KEeOHFFBQYHsdrsGDx6sJUuW6IorrlBlZaVcLpdKSkp8de12u4YPH66dO3fqgQceCPh8DQ0Namg4vxLE4/FEo9lR1Zn5H53V4cMBN/5Ievt3pqLPC0brjhOzpfrzddNsUrDve+8QUnubv3lF4zrF89oDADom4gFl8ODBeuGFF3T11Vfr888/1+OPP65hw4bp4MGDcrlckqS8vDzTz+Tl5emTTz4J+pxLly7V4sWLI93UmAm0f4gU/pd3R3XocMAAvSb6ly+Vl56hilYrgW4ovETffHKbvjrd6Pcjhlr2Plm84ZDG9ne2uwlcpK9TvK89AKBjIj7EM378eN11110aMGCAxowZo9dff12StGrVKl8dm838JWUYhl/ZhRYuXCi32+27HTt2LNLNjppIzP/orLAPBwwUTsrcUnqGpPObu02+4VINvbKH9h07GTCceLU5hPS1aFwnK1x7AEDHRP0snm7dumnAgAE6cuSIbzWPtyfFq7q62q9X5UJ2u105OTmmW6Lo1PyPCAn5cMDP3wseTtrQ4SGkC0TjOlnh2gMAOibqAaWhoUHvv/++8vPzVVRUJKfTqS1btvgeb2xsVHl5uYYNGxbtpsRFJL68I6HdwwH/eK3077eZf+gHb7YbTqQODiG1Eo3rZJVrDwAIX8TnoCxYsEATJ07UZZddpurqaj3++OPyeDyaMWOGbDabSktLtWTJEvXr10/9+vXTkiVLdNFFF2n6dP+dSZNBJL68w9HWTrFBDwf82SX+TxRCMPHyDiG53PUBh1NCORwwGtcp1tceABA5EQ8ox48f1913360vv/xSvXr10pAhQ7Rr1y717dtXkvTwww+rrq5ODz30kGpqajR48GBt3rxZ2dnZkW6KJUTiy/tCbQWQUFarmA4HbG6SOhlOvM+5aGJ/zVq9t8OHA0b6OkXrOQEAsWEzDCPhZgh6PB45HA653e6EmI/iXUkiBf7yjsQSXEkBV6sEfY2tZVLFs+bKQx6Sxi1ttx0daV+o7y8S1ynazwkA6Jhwvr8JKDESqS/vQAHEUMvJwSfPnA34s96egopHRrX0YgRZQuxdpdMZHT2M0It9UAAgeRFQLKqjX95NzYZufXJbmytSQvGHHwwJuGV9uEM60dbZkBOr5wQAhCec729OM44h0/yPMLS3XDYUl9uqEiKcSB2/TrF+TsDKCOVIdASUBNDZZbB/tc/VpbYT5sIHdkj5Azv1vACsiWFNJIOo74OCzuvMMtijWdP9w0mZO2LhhEP4AGvxzldr3evqPd4h3MM7gXihByUBtLdcNhCbmlWZdY//AxEc0uGvNMBa2jveIdRzsQAroAclAbS1VX0gw9IO+IWTJ89+T2/d+3HE2sRfaYD1cLwDkgkBJUEE26q+tV322VqTucRUdkX9ai1vmhSxLd05hA+wJo53QDIhoCSQccX5qnhklF76p8G6pKv/niVHs6bLaasxlV1ev0bNX/+aI7WlO3+lAdbE8Q5IJgSUBJOeZtM3r+qpJ+4aIJtahnyydUZHs8xnGa04d7sur18jfV0nP4JbuvNXGmBN3vlqwYaCI/1vARBNBJQE5R3y+f7Fb2t/1j+ZHrux/rdafG6GpNDPwgkHf6UB1tTWfLVo/FsARBMBJYGN+/NILT73S1PZkMx1+krnd+dzOrIift4Mf6UB1hVsvlo0/i0AoollxonIMKTFl5jLBt4t3flb/TUGu0dG4vRiANEzrjhfY/s72UkWCY2zeBLN54ek5UPNZf9cLhXcEPOmsA8KACAcnMWTrDaUSntWmMt+WiOlxWakrvXZHmP7O/krDQAQFQSURFHmMN/PvFh69NOYvTy9JQCAWGKSrNXVu/3DyaRlMQ8n7BoLAIglAoqVHdkqPXGZueyRo9KN98asCewaCwCIBwKKVe38N+mlu8xlZW6pa/eYNoNdYwEA8cAcFKsxDGnZTdKJD8+X3fCP0h2/iUtz2DUWABAPBBQrOfWF9P9dZS6bf1jKdsanPWLXWABAfDDEYxWHN5nDiaNQWnQyruFEYtdYAEB8EFCsYP2D0h+mnb8/6l+k/3lAssV/PxHO9gAAxAMBJZ7ONbQsIf6vP5wve2CHdNuC+LUpAM72AADEGnNQ4uXzg9LyYeayx1xSRtf4tKcdnO0BAIglAko87FwmbX7s/P3+k6WpL8SvPSFKT7Np6JU94t0MAEAKIKBcoPVZMxHvITAM6de3SF9+cL5s6otS/0mRew0AAJIAAeVrUT9rxoJLiAEAsComySoGZ8188GdzOMm51BJLiAEAsKqUDyhRP2tm/YPSmqnn74/6iTTvkCWWEAMAYFUpP8QTzlkzYU8QffJyqa7m/P1/LpcKbuhIMwEASCkpH1CidtZMQ605nDxaJWVeFN5zAACQolI+oETtrBl7tvTdlVLTWen6qe1WBwAA56V8QPGeNeNy1wech2JTy46pHTpr5ro7O9s8AABSUspPkuWsGQAArCflA4rEWTMAAFhNyg/xeHHWDAAA1kFAuQBnzQAAYA0M8QAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMtJyJ1kDaPl3GGPxxPnlgAAgFB5v7e93+NtSciAUltbK0kqLCyMc0sAAEC4amtr5XA42qxjM0KJMRbT3Nyszz77TNnZ2bLZwj/Mz+PxqLCwUMeOHVNOTk4UWphcuF6h41qFjmsVOq5VeLheoYv1tTIMQ7W1tSooKFBaWtuzTBKyByUtLU19+vTp9PPk5OTw4Q0D1yt0XKvQca1Cx7UKD9crdLG8Vu31nHgxSRYAAFgOAQUAAFhOSgYUu92uRYsWyW63x7spCYHrFTquVei4VqHjWoWH6xU6K1+rhJwkCwAAkltK9qAAAABrI6AAAADLIaAAAADLIaAAAADLScmA8pvf/EZFRUXKysrSoEGD9Je//CXeTYq7srIy2Ww2083pdPoeNwxDZWVlKigoUNeuXTVixAgdPHgwji2OnR07dmjixIkqKCiQzWbTK6+8Yno8lGvT0NCguXPnqmfPnurWrZsmTZqk48ePx/BdxEZ71+q+++7z+5wNGTLEVCdVrtXSpUt18803Kzs7W71799Ydd9yhw4cPm+rw2WoRyrXis3Xe8uXLdf311/s2Xxs6dKjeeOMN3+OJ8rlKuYDy8ssvq7S0VI899pjeffddfetb39L48eP197//Pd5Ni7vrrrtOVVVVvtv+/ft9jz311FN65plntGzZMu3evVtOp1Njx471nYuUzE6fPq2BAwdq2bJlAR8P5dqUlpZq/fr1Wrt2rSoqKnTq1ClNmDBBTU1NsXobMdHetZKkcePGmT5nGzduND2eKteqvLxcs2fP1q5du7RlyxadO3dOJSUlOn36tK8On60WoVwric+WV58+ffTEE0/onXfe0TvvvKNRo0Zp8uTJvhCSMJ8rI8XccsstxoMPPmgqu/baa40f//jHcWqRNSxatMgYOHBgwMeam5sNp9NpPPHEE76y+vp6w+FwGL/97W9j1EJrkGSsX7/edz+Ua3Py5EkjIyPDWLt2ra/Op59+aqSlpRmbNm2KWdtjrfW1MgzDmDFjhjF58uSgP5Oq18owDKO6utqQZJSXlxuGwWerLa2vlWHw2WpP9+7djd///vcJ9blKqR6UxsZG7dmzRyUlJabykpIS7dy5M06tso4jR46ooKBARUVF+t73vqePP/5YklRZWSmXy2W6bna7XcOHD0/56xbKtdmzZ4/Onj1rqlNQUKDi4uKUvH7bt29X7969dfXVV+sHP/iBqqurfY+l8rVyu92SpNzcXEl8ttrS+lp58dny19TUpLVr1+r06dMaOnRoQn2uUiqgfPnll2pqalJeXp6pPC8vTy6XK06tsobBgwfrhRde0J///Gc999xzcrlcGjZsmE6cOOG7Nlw3f6FcG5fLpczMTHXv3j1onVQxfvx4vfTSS9q2bZuefvpp7d69W6NGjVJDQ4Ok1L1WhmFo3rx5uvXWW1VcXCyJz1Ywga6VxGertf379+viiy+W3W7Xgw8+qPXr16t///4J9blKyNOMO8tms5nuG4bhV5Zqxo8f7/vvAQMGaOjQobryyiu1atUq30QzrltwHbk2qXj9pk2b5vvv4uJi3XTTTerbt69ef/11TZkyJejPJfu1mjNnjt577z1VVFT4PcZnyyzYteKzZXbNNddo3759OnnypP70pz9pxowZKi8v9z2eCJ+rlOpB6dmzp9LT0/0SYHV1tV+aTHXdunXTgAEDdOTIEd9qHq6bv1CujdPpVGNjo2pqaoLWSVX5+fnq27evjhw5Iik1r9XcuXP12muv6c0331SfPn185Xy2/AW7VoGk+mcrMzNTV111lW666SYtXbpUAwcO1K9+9auE+lylVEDJzMzUoEGDtGXLFlP5li1bNGzYsDi1ypoaGhr0/vvvKz8/X0VFRXI6nabr1tjYqPLy8pS/bqFcm0GDBikjI8NUp6qqSgcOHEj563fixAkdO3ZM+fn5klLrWhmGoTlz5mjdunXatm2bioqKTI/z2TqvvWsVSCp/tgIxDEMNDQ2J9bmK2XRci1i7dq2RkZFhPP/888ahQ4eM0tJSo1u3bsbRo0fj3bS4mj9/vrF9+3bj448/Nnbt2mVMmDDByM7O9l2XJ554wnA4HMa6deuM/fv3G3fffbeRn59veDyeOLc8+mpra413333XePfddw1JxjPPPGO8++67xieffGIYRmjX5sEHHzT69OljbN261di7d68xatQoY+DAgca5c+fi9baioq1rVVtba8yfP9/YuXOnUVlZabz55pvG0KFDjUsvvTQlr9WsWbMMh8NhbN++3aiqqvLdzpw546vDZ6tFe9eKz5bZwoULjR07dhiVlZXGe++9Zzz66KNGWlqasXnzZsMwEudzlXIBxTAM49e//rXRt29fIzMz07jxxhtNS9VS1bRp04z8/HwjIyPDKCgoMKZMmWIcPHjQ93hzc7OxaNEiw+l0Gna73bjtttuM/fv3x7HFsfPmm28akvxuM2bMMAwjtGtTV1dnzJkzx8jNzTW6du1qTJgwwfj73/8eh3cTXW1dqzNnzhglJSVGr169jIyMDOOyyy4zZsyY4XcdUuVaBbpOkowVK1b46vDZatHeteKzZXb//ff7vuN69epljB492hdODCNxPlc2wzCM2PXXAAAAtC+l5qAAAIDEQEABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACW8/8DtFN1LIsQ3g4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(y_hat_merf, y_test, \"o\")\n",
+    "plt.plot(y_test, y_test, \"-\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "739e6057-e474-4d18-8e77-6c8281ad0f07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "r2 summary:\n",
+      "Linear:  0.6247992212591051\n",
+      "RF:  0.8024510481561682\n",
+      "EBM:  0.799262187206355\n",
+      "MERF:  0.8286050862891772\n",
+      "RF->MERF:  0.8224138884400743\n",
+      "Linear->MERF:  0.6650943700720995\n",
+      "EBM->MERF:  0.7881151017126167\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"r2 summary:\")\n",
+    "print(\"Linear: \", r2_score(y_test, y_hat_lm))\n",
+    "print(\"RF: \", r2_score(y_test, y_hat_rf))\n",
+    "print(\"EBM: \", r2_score(y_test, y_hat_ebm))\n",
+    "print(\"MERF: \", r2_score(y_test, y_hat_merf))\n",
+    "print(\"RF->MERF: \", r2_score(y_test, y_hat_merf_rf))\n",
+    "print(\"Linear->MERF: \", r2_score(y_test, y_hat_merf_lm))\n",
+    "print(\"EBM->MERF: \", r2_score(y_test, y_hat_merf_ebm))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb2959fc-7291-406d-a22f-f21081d124b4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Springtime x86",
+   "language": "python",
+   "name": "springtime_x86"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 2893e61086d2e32c1056f4bd2a069670c8c35740 Mon Sep 17 00:00:00 2001
From: khzadeh <m.khodadadzadeh@utwente.nl>
Date: Thu, 4 May 2023 14:57:26 +0200
Subject: [PATCH 5/5] merf and ebm dependencies added

---
 pyproject.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 161f8efe..312cbd91 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,6 +45,8 @@ dependencies = [
   "geopandas",
   "dask",  # needed for xarray.open_mfdataset()
   "netCDF4",
+  "interpret",
+  "merf",
 ]
 dynamic = ["version"]