Simple Decision Tree Implementation (Java)

A lightweight implementation of a Decision Tree built entirely from scratch in Java to learn basic machine learning principles.
This project demonstrates the core principles behind tree-based machine learning algorithms such as ID3, using a simple dataset which can be altered.

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Key components:

• Node Class – Represents each node in the tree (feature, threshold, children, or leaf value).
• DecisionTree Class – Handles the training (fit) and prediction (predict) logic.
• Main Class – Provides a simple demonstration using example data.

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Features

• Builds a decision tree without external ML libraries
• Uses information gain to find the best splits
• Easily integratable into more advanced models like Random Forests