Decision Tree Implementation from Scratch in Python

This repository contains a Python implementation of a decision tree algorithm developed from first principles, without relying on any machine learning libraries. Decision trees are powerful and interpretable models widely used for classification and regression tasks. This project aims to provide a fundamental understanding of decision tree principles and demonstrate their implementation in Python.

Decision Trees

Decision trees are hierarchical tree-like structures that recursively partition the feature space based on the values of input features and their corresponding labels. Each internal node of the tree represents a decision based on a feature, and each leaf node represents a class label or a regression value. The decision tree algorithm learns to make decisions by selecting the most informative features and determining the optimal splitting criteria to maximize information gain or minimize impurity.

Advantages of Decision Trees

• Interpretability: Decision trees provide a clear and intuitive representation of decision-making processes, making them easy to interpret and understand.
• Feature Importance: Decision trees can automatically rank features based on their importance in predicting the target variable, providing insights into the underlying data patterns.
• Non-linear Relationships: Decision trees can capture non-linear relationships and interactions between features without requiring complex transformations.
• Robustness to Irrelevant Features: Decision trees are robust to irrelevant features and can effectively handle datasets with a large number of features.

Disadvantages of Decision Trees

• Overfitting: Decision trees are prone to overfitting, especially when the tree depth is not properly controlled or when the dataset is noisy.
• High Variance: Decision trees can exhibit high variance, leading to unstable predictions when trained on small datasets or datasets with imbalanced classes.
• Limited Expressiveness: Decision trees may struggle to capture complex decision boundaries or relationships compared to more sophisticated algorithms such as ensemble methods or neural networks.
• Greedy Nature: Decision trees use a greedy approach to select splitting criteria at each node, which may not always lead to globally optimal solutions.

Project Structure

• decision_tree.py: Contains the implementation of the decision tree algorithm from scratch. Includes unit tests for validating the correctness and performance of the decision tree implementation.
• README.md: Overview of the project, description of decision trees, and discussion of their advantages and disadvantages.

How to Use

1. Clone this repository to your local machine.
2. Run decision_tree.py to execute the unit tests and validate the correctness of the decision tree implementation.
3. Explore the decision_tree.py file to understand the inner workings of the decision tree algorithm and its implementation details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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