Python3 implementation of Decision Tree and Random Forest (gini-index as the attribute selection measure) for classification
Test and Training Files can be found in the data folder. In both implementations there are already tuned hyperparamaters for every data set in the data folder. If new data sets are included, the hyperparamters have to be added to the implementations.
Usage (DecisionTree): python3 DecisionTree.py train_file test_file
Usage (RandomForst): python3 RandomForest.py train_file test_file
Output: k*k (k = count of labels) matrix representing the confusion matrix of the classifier on testing data
Example usage (with data from the data folder): python3 RandomForest balance.scale.train balance.scale.test