sklears-feature-selection

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

Overview

sklears-feature-selection brings the complete scikit-learn feature selection toolbox to Rust, including filter, wrapper, and embedded methods. The crate underpins AutoML workflows, feature pipelines, and inspection utilities across the sklears project.

Key Features

• Filter Methods: VarianceThreshold, mutual information, ANOVA F-tests, chi-square tests, and more.
• Wrapper Methods: RFE/RFECV, SequentialFeatureSelector, model-based selectors with parallel evaluation.
• Embedded Techniques: L1-based selection, tree-based importance, stability selection, and feature importance scoring.
• Streaming & GPU Support: Optional streaming evaluators and CUDA/WebGPU acceleration for heavy scoring tasks.

Quick Start

use sklears_feature_selection::{SequentialFeatureSelector, Strategy};
use sklears_linear::LogisticRegression;

let estimator = LogisticRegression::builder()
    .max_iter(200)
    .multi_class("auto")
    .build();

let selector = SequentialFeatureSelector::builder()
    .estimator(estimator)
    .strategy(Strategy::Forward)
    .n_features_to_select(5)
    .n_jobs(4)
    .build();

let fitted = selector.fit(&x_train, &y_train)?;
let x_selected = fitted.transform(&x_train)?;

Status

• Covered by the 11,292 passing workspace tests executed for 0.1.0-beta.1.
• Supports >99% of scikit-learn’s feature selection API surface.
• Additional milestones (distributed scoring, SHAP-guided selection) tracked in this crate’s TODO.md.