sklearn-rs 0.1.0

A scikit-learn inspired machine learning library in Rust
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# sklearn-rs


[![Crates.io](https://img.shields.io/crates/v/sklearn-rs.svg)](https://crates.io/crates/sklearn-rs)
[![Documentation](https://docs.rs/sklearn-rs/badge.svg)](https://docs.rs/sklearn-rs)

A scikit-learn inspired machine learning library in Rust.

## Features


- Linear Regression
- Evaluation Metrics (MSE, MAE, R²)
- Type-safe API
- Comprehensive error handling

## Quick Start


```toml
[dependencies]
sklearn-rs = "0.1.0"

use sklearn_rs::{LinearRegression, Estimator, Predictor};
use ndarray::array;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let x = array![[1.0], [2.0], [3.0]];
    let y = array![2.0, 4.0, 6.0];
    
    let model = LinearRegression::default().fit(&x, &y)?;
    let predictions = model.predict(&x)?;
    
    println!("Predictions: {:?}", predictions);
    Ok(())
}
License
MIT


# sklearn-rs


[![Crates.io](https://img.shields.io/crates/v/sklearn-rs.svg)](https://crates.io/crates/sklearn-rs)
[![Documentation](https://docs.rs/sklearn-rs/badge.svg)](https://docs.rs/sklearn-rs)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

一个受 scikit-learn 启发的 Rust 机器学习库。

## 功能特性


- 🚀 **线性回归** - 完整的线性回归实现
- 📊 **评估指标** - MSE, MAE, R² 分数
- 🔒 **类型安全** - Rust 强类型系统保障
- 🛡️ **错误处理** - 完善的验证和错误信息
- 📚 **中文文档** - 完整的中文文档支持

## 安装


在 `Cargo.toml` 中添加：

```toml
[dependencies]
sklearn-rs = "0.1.0"
ndarray = "0.15"