The DecisionTree.py file is a Python script that implements a decision tree machine learning algorithm. The decision tree can be used for both classification and regression tasks.
You can run this code by executing the script in a Python environment. Make sure you have the necessary dependencies installed (see the "Dependencies" section below). You can run the script with the following command:
python DecisionTree.py
Note: This command assumes that you have Python installed and that the script is in your current working directory.
The script requires the following Python libraries:
- NumPy
- Pandas
You can install these dependencies using pip:
pip install numpy pandas
The script primarily contains a class called DecisionTree that encapsulates the decision tree algorithm. The class includes methods for fitting the model to data (fit), predicting the target variable for given data points (predict), and several private methods used for constructing the tree.
The DecisionTree class uses a nested Node class to represent the nodes in the decision tree. Each Node can represent either a decision node (with a feature and a threshold for splitting) or a leaf node (with a value to be returned for predictions).
Entropy is a measure of disorder or uncertainty and the goal of Machine Learning models and Data Mining is to reduce uncertainty. The entropy is calculated using the formula:
H(X)=−∑(P(x)∗log2(P(x)))
where P(x) is the probability of occurrence of data element x.
- Pruning: Implement pruning to avoid overfitting and reduce the complexity of the final decision tree.
- Handling Missing Values: Enhance the decision tree to handle missing values in the input data.
- Support for Categorical Variables: Currently, the decision tree only supports numerical variables. It could be extended to support categorical variables as well.