Homemade ID3 Decision Tree Learning

Overview

This project involved implementing the ID3 decision tree algorithm from scratch to develop a deeper understanding of entropy, information gain, and recursive model construction. Rather than relying on libraries, I focused on translating theoretical concepts into correct, explainable code.

Problem & Context

While libraries abstract away algorithmic details, implementing a decision tree manually exposes important design choices and pitfalls. The goal was to translate theory into correct, explainable code that demonstrates understanding of how decision trees actually work.

Understanding machine learning algorithms requires more than using libraries: it requires understanding how they work under the hood. This project aimed to bridge the gap between theoretical understanding and practical implementation.

Constraints

• No ML libraries for tree construction (NumPy only for basic operations)
• Must correctly handle base cases and stopping criteria
• Code should remain readable and explainable
• Must handle edge cases like pure nodes and attribute exhaustion
• Not designed for large datasets or production use

Approach & Design Decisions

I built the tree recursively using entropy and information gain calculations, focusing on correctness and clarity rather than optimization. The design followed the theoretical ID3 algorithm structure:

1. Entropy Calculation: Implemented entropy as a measure of impurity in datasets
2. Information Gain: Calculated information gain for each attribute to determine optimal splits
3. Recursive Construction: Built the tree recursively, selecting optimal attributes at each node
4. Base Cases: Handled stopping conditions (pure nodes, no attributes remaining)

The recursive approach matched the theoretical description of ID3 and made the code structure align with the algorithm's natural structure.

Implementation Highlights

• Explicit Entropy Function: Implemented entropy calculation from first principles
• Information Gain Logic: Built information gain calculation to guide attribute selection
• Recursive Tree Construction: Implemented recursive tree building with proper base case handling
• Edge Case Handling: Addressed pure nodes, attribute exhaustion, and empty datasets

# Code coming soon...
# Implementation details will be added here

Results & Evaluation

The implementation produced correct trees on test datasets and aligned with expected theoretical behavior. The trees:

• Correctly classified test data according to decision rules
• Demonstrated clear information gain calculations at each split
• Produced interpretable models where decision paths were transparent
• Validated against known correct implementations and theoretical expectations

The project successfully demonstrated understanding of entropy, information gain, and recursive algorithm design.

Tradeoffs & Limitations

• No Pruning: The implementation doesn't include post-pruning, which can lead to overfitting
• Not Production-Ready: Not designed for large datasets or production use cases
• Discrete Attributes Only: Handles only discrete-valued attributes, not continuous
• No Optimization: Focused on correctness and clarity rather than performance optimization

What I Learned

Writing algorithms from scratch clarified how small design decisions impact correctness and interpretability. Key insights:

1. Algorithm Correctness: Implementing from scratch revealed subtle details about edge cases and base conditions
2. Theoretical Translation: Converting mathematical concepts (entropy, information gain) into code requires careful thought
3. Debugging Recursion: Debugging recursive algorithms requires systematic testing of base cases and recursive steps
4. Model Interpretability: Building models yourself makes their behavior transparent and understandable

The experience reinforced that understanding algorithms deeply requires implementing them, not just using them.