Decision Tree Classifier

Introduction

Welcome to the documentation for the DecisionTree classifier. This code implements a decision tree algorithm for classification tasks. The decision tree is built based on the principles of information gain and impurity measures, such as entropy and Gini impurity.

Features

• Decision Tree Algorithm: The classifier is built using a decision tree algorithm that recursively splits the data based on the features and thresholds that maximize information gain or reduce impurity.
• Support for Entropy and Gini Impurity: The classifier supports both entropy and Gini impurity as impurity measures for splitting the data.
• Customizable Parameters: You can customize various parameters of the decision tree, such as the minimum number of samples required to split a node, the maximum depth of the tree, and the impurity measure to be used.
• Efficient Implementation: The code has been optimized for efficiency, ensuring fast training and prediction times even for large datasets.

Code Structure

The code is organized as follows:

• calculate_entropy(labels): Function to calculate the entropy of a set of labels.
• calculate_gini(labels): Function to calculate the Gini impurity of a set of labels.
• DecisionNode: Class representing a node in the decision tree. It contains information about the feature index, threshold, value (for leaf nodes), and the branches for true and false conditions.
• DecisionTree: Class implementing the decision tree classifier. It contains methods for splitting the data, calculating impurity, finding the best split, building the tree, fitting the model, and making predictions.

Usage

To use the DecisionTree classifier, follow these steps:

1. Import the necessary libraries, including numpy.
2. Create an instance of the DecisionTree class, optionally specifying the desired parameters.
3. Fit the model by calling the fit(X, y) method, where X is the input features and y is the corresponding labels.
4. Use the trained model to make predictions by calling the predict(X) method, where X is the input features for prediction.

Here is an example of how to use the DecisionTree classifier:

from decision_tree import DecisionTree

# Create the decision tree classifier
classifier = DecisionTree(min_samples_split=5, max_depth=3, impurity='entropy')

# Fit the model to the training data
X_train = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y_train = np.array([0, 1, 0])
classifier.fit(X_train, y_train)

# Make predictions on new data
X_test = np.array([[2, 3, 4], [5, 6, 7]])
predictions = classifier.predict(X_test)

print("Predictions:", predictions)

Conclusion

The DecisionTree classifier provides a powerful and flexible tool for classification tasks. It leverages the principles of decision tree algorithms and impurity measures to construct a model that can make accurate predictions on new data.