sklears-ensemble

Latest release: 0.1.0-beta.1 (January 1, 2026). See the workspace release notes for highlights and upgrade guidance.

Overview

sklears-ensemble delivers bagging, boosting, stacking, voting, and random forest implementations with scikit-learn parity and Rust-first performance.

Key Features

• Tree Ensembles: RandomForest, ExtraTrees, GradientBoosting, Histogram-based boosting, IsolationForest.
• Linear/Stochastic Ensembles: Bagging, AdaBoost, Stacking, Voting, Snapshot ensembles, warm-starting.
• GPU + SIMD: Accelerated split finding, batched inference, and quantized histograms.
• AutoML Integration: Works with feature selection, model selection, and inspection crates for end-to-end workflows.

Quick Start

use sklears_ensemble::RandomForestClassifier;
use scirs2_core::ndarray::{array, Array1};

let x = array![
    [0.0, 1.0, 2.0],
    [1.0, 0.5, 2.1],
    [0.5, 2.0, 1.5],
];
let y = Array1::from(vec![0, 1, 0]);

let forest = RandomForestClassifier::builder()
    .n_estimators(500)
    .max_depth(Some(10))
    .n_jobs(-1)
    .bootstrap(true)
    .build();

let fitted = forest.fit(&x, &y)?;
let predictions = fitted.predict(&x)?;

Status

• Included in the 11,292 passing workspace tests for 0.1.0-beta.1.
• Benchmarks demonstrate 5–30× faster training versus scikit-learn on medium to large datasets.
• Roadmap items (GPU GradientBoosting, federated ensembles) live in this crate’s TODO.md.