[TESTING] html widgets in notebooks

site/notebooks/EXECUTED/model_development/2-start_development_process.ipynb

@@ -811,22 +811,25 @@
    "outputs": [],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import GridSearchCV\n",
     "\n",
-    "# Logistic Regression grid params\n",
     "log_reg_params = {\n",
-    "    \"penalty\": [\"l1\", \"l2\"],\n",
+    "    \"l1_ratio\": [0.0, 1.0],  # 0 = L2, 1 = L1\n",
     "    \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
-    "    \"solver\": [\"liblinear\"],\n",
     "}\n",
     "\n",
-    "# Grid search for Logistic Regression\n",
-    "from sklearn.model_selection import GridSearchCV\n",
+    "grid_log_reg = GridSearchCV(\n",
+    "    LogisticRegression(\n",
+    "        solver=\"saga\",\n",
+    "        penalty=\"elasticnet\",   # required when using l1_ratio\n",
+    "        max_iter=5000,\n",
+    "    ),\n",
+    "    log_reg_params,\n",
+    ")\n",
     "\n",
-    "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
     "grid_log_reg.fit(X_train, y_train)\n",
     "\n",
-    "# Logistic Regression best estimator\n",
-    "log_reg = grid_log_reg.best_estimator_"
+    "log_reg = grid_log_reg.best_estimator_\n"
    ]
   },
   {

site/notebooks/EXECUTED/test.ipynb

@@ -0,0 +1,19 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9a8bce10",
+   "metadata": {},
+   "source": [
+    "# test changed notebook"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

site/tests/model_validation/statsmodels/CumulativePredictionProbabilities.md

@@ -1,39 +1,35 @@
 # CumulativePredictionProbabilities
 
-Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models.
+Visualizes cumulative probabilities of positive and negative classes in classification models.
 
 ### Purpose
 
 This metric is utilized to evaluate the distribution of predicted probabilities for positive and negative classes
 in a classification model. It provides a visual assessment of the model's behavior by plotting the cumulative
-probabilities for positive and negative classes across both the training and test datasets.
+probabilities for positive and negative classes within the provided dataset.
 
 ### Test Mechanism
 
-The classification model is evaluated by first computing the predicted probabilities for each instance in both
-the training and test datasets, which are then added as a new column in these sets. The cumulative probabilities
-for positive and negative classes are subsequently calculated and sorted in ascending order. Cumulative
-distributions of these probabilities are created for both positive and negative classes across both training and
-test datasets. These cumulative probabilities are represented visually in a plot, containing two subplots - one for
-the training data and the other for the test data, with lines representing cumulative distributions of positive and
-negative classes.
+The classification model is evaluated by first computing the predicted probabilities for each instance in the
+dataset, which are then added as a new column. The cumulative probabilities for positive and negative classes are
+subsequently calculated and sorted in ascending order. Cumulative distributions of these probabilities are created
+for both positive and negative classes. These cumulative probabilities are represented visually in a plot with lines
+representing cumulative distributions of positive and negative classes.
 
 ### Signs of High Risk
 
 - Imbalanced distribution of probabilities for either positive or negative classes.
-- Notable discrepancies or significant differences between the cumulative probability distributions for the
-training data versus the test data.
 - Marked discrepancies or large differences between the cumulative probability distributions for positive and
 negative classes.
+- Unusual patterns in the cumulative probability distributions that may indicate model calibration issues.
 
 ### Strengths
 
 - Provides a visual illustration of data, which enhances the ease of understanding and interpreting the model's
 behavior.
-- Allows for the comparison of model's behavior across training and testing datasets, providing insights about how
-well the model is generalized.
 - Differentiates between positive and negative classes and their respective distribution patterns, aiding in
 problem diagnosis.
+- Helps identify potential calibration issues by visualizing how probabilities are distributed across classes.
 
 ### Limitations
 
@@ -42,5 +38,4 @@ problem diagnosis.
 detection.
 - The method does not give a solitary quantifiable measure of model risk, instead, it offers a visual
 representation and broad distributional information.
-- If the training and test datasets are not representative of the overall data distribution, the metric could
-provide misleading results.
+- If the dataset is not representative of the overall data distribution, the metric could provide misleading results.