sklears-multioutput

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

Overview

sklears-multioutput implements multi-output regression and classification wrappers that allow any estimator to generalize to multi-label and multi-output settings, mirroring scikit-learn’s multioutput module.

Key Features

• Wrappers: MultiOutputRegressor, MultiOutputClassifier, ClassifierChain, RegressorChain.
• Parallelism: Multi-threaded fitting of per-target estimators with shared caching.
• Integration: Works with pipelines, model selection, and calibration components out of the box.
• Advanced Modes: Supports probabilistic chaining, custom meta-estimators, and GPU-enabled base learners.

Quick Start

use sklears_multioutput::MultiOutputRegressor;
use sklears_linear::Ridge;

let base_estimator = Ridge::builder()
    .alpha(1.0)
    .fit_intercept(true)
    .build();

let wrapper = MultiOutputRegressor::new(base_estimator);
let fitted = wrapper.fit(&x_train, &y_train)?;
let predictions = fitted.predict(&x_test)?;

Status

• Validated by the overall 11,292 passing workspace tests for 0.1.0-beta.1.
• Ensures full parity with scikit-learn’s multi-output utilities while leveraging Rust’s performance.
• Future enhancements (asynchronous chaining, probabilistic calibration) tracked in TODO.md.