Decision Tree

A Rust library for building and using decision trees for classification. This library supports both Gini and Entropy criteria for building decision trees.

Features

• Build decision trees for classification
• Support for Gini impurity and entropy-based information gain
• Flexible configuration for minimum samples split, impurity threshold, and maximum depth

Installation

To use this library in your project, add the following line to your Cargo.toml file:

[dependencies]
decision_tree = { git = "https://github.com/abedinia/rust_decision_tree.git" }

Usage

Here is an example of how to use the DecisionTree and ClassificationTree structs to build and use a decision tree for classification.

use decision_tree::classification_tree::ClassificationTree;

fn main() {
    let X = vec![
        vec![2.0, 3.0],
        vec![1.0, 1.0],
        vec![4.0, 5.0],
        vec![6.0, 7.0],
    ];
    let y = vec![0.0, 1.0, 0.0, 1.0];

    let mut tree = ClassificationTree::new("GINI", 2, 1e-7, usize::MAX);
    tree.fit(X.clone(), y.clone());
    let predictions = tree.predict(X);
    println!("{:?}", predictions);
}

Modules

DecisionNode

The DecisionNode struct represents a node in the decision tree.

DecisionTree

The DecisionTree struct provides methods for building and using decision trees.

ClassificationTree

The ClassificationTree struct builds a decision tree for classification purposes.

Testing

To run the tests for this library, use the following command:

cargo test