A breakdown of the project including objectives and learning outcomes available on my website
The zip file contains the code for the problem that has been stated in the pdf labeled "hw1"
code submitted by: PARTH PADALKAR. UTD ID 2021473758
language : Python2.7 library requirements: Pandas numpy scikit-learn
The program runs the algorithms one after the other
#commandline arguments:
-train_data : used to input the training data path -test_data : used to input the testing data path -valid_data : used to input the validataion data path -alorithm_number : used to input the algorithm that you wish to run on the data
1: naive decision tree with entropy heuristic 2: naive decision tree with variance heuristic 3: decision tree with entropy heuristic and reduced error pruning 4: decision tree with variance heuristic and reduced error pruning 5: decision tree with entropy heuristic and depth based pruning 6: decision tree with variance heuristic and depth based pruning 7: Random forset
example command
python d_tree.py -algorithm_number 2 -train_data "all_data/train_c300_d100.csv" -valid_data "all_data/valid_c300_d100.csv" -test_data "all_data/test_c300_d100.csv"