sklears-multioutput

Latest release: 0.2.0 (July 14, 2026). See the workspace release notes for highlights and upgrade guidance.

Overview

sklears-multioutput implements meta-estimators for multi-target regression and classification — independent per-target prediction (MultiOutputRegressor/MultiOutputClassifier), classifier/regressor chains, and multi-label utilities — mirroring scikit-learn's multioutput module.

Key Features

• Estimators: MultiOutputRegressor, MultiOutputClassifier, ClassifierChain, RegressorChain, and EnsembleOfChains.
• Parallelism: Multi-threaded fitting of per-target models via n_jobs.
• Integration: Works with pipelines, model selection, and calibration components out of the box.
• Advanced Modes: Probabilistic (Bayesian-inference) classifier chains and configurable chain ordering/cross-validation.

Quick Start

use sklears_multioutput::MultiOutputRegressor;
use sklears_core::traits::{Fit, Predict};
use scirs2_core::ndarray::array;

let x_train = array![[1.0, 2.0], [2.0, 3.0], [3.0, 1.0], [4.0, 5.0]];
let y_train = array![[1.5, 2.5], [2.5, 3.5], [2.0, 1.5], [3.0, 4.0]];

let wrapper = MultiOutputRegressor::new().n_jobs(Some(4));
let fitted = wrapper.fit(&x_train.view(), &y_train)?;
let predictions = fitted.predict(&x_train.view())?;

Status

• Validated by 246 passing tests in 0.2.0 (Stable).
• 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.