Implementation of the classification decision tree and random forest supporting both gini and entropy criteria, and max_depth,
min_to_split and max_leaves constraints.
Random forest is a collection of decision trees trained with dataset bagging and random feature subsampling.
Task from Machine Learning for Greenhorns – Winter 2020/21. Part of this code was provided by Milan Straka.
The decision tree algorithm is implemented in decision_tree.py.
The file decision_tree_main.py shows how to use the implemented algorithm on some artificial data.
The example of the invocation of the program are:
python decision_tree_main.py --criterion=gini --min_to_split=40 --max_leaves=4 --seed=92
python decision_tree_main.py --criterion=entropy --min_to_split=55 --seed=97
python decision_tree_main.py --criterion=entropy --min_to_split=45 --max_depth=2 --seed=100
The random forest is implemented in random_forest.py.
The file random_forest_main.py shows how to use the implemented algorithm on some artificial data.
The example of the invocation of the program are:
python random_forest_main.py --trees=3 --bootstrapping --feature_subsampling=0.5 --max_depth=2 --seed=46
python random_forest_main.py --trees=3 --bootstrapping --max_depth=2 --seed=46
python random_forest_mian.py --trees=3 --feature_subsampling=0.5 --max_depth=2 --seed=46