Lib.rs

Summary

An easy to use and learn ML toolkit for Rust

Features

• Simple and intuitive API for common Machine Learning tasks.
• Implementations of popular algorithms like K-Nearest Neighbors and Decision Trees.
• Support for classification, regression, and clustering.
• Utility functions for data manipulation and metrics evaluation.
• Includes sample datasets like Iris, Housing, and Breast Cancer for quick experimentation.

Installation

Add Rusty Science to your Cargo.toml dependencies:

[dependencies]
rusty_science = "0.1.1"

Usage

use rusty_science::classification::KNNClassifier;
use rusty_science::data::load_iris;

fn main() {
    let iris_data = load_iris();
    let (data, labels) = iris_data.to_numerical_labels();

    let target = vec![1.5, 1.5, 1.5, 1.5];

    let n_neighbors = 3;
    let knn = KNNClassifier::<f64, i64>::new(n_neighbors);
    knn.fit(data, labels);
    let prediction = knn.predict(target);
}

Note: This crate is a work in progress and features are subject to change

Implementation table

Features:

Metrics:

Datasets:

Contact

If you want to contact us email us at cooper.brown197@gmail.com or jack.welsh@drake.edu