sklears-discriminant-analysis

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

Overview

sklears-discriminant-analysis implements Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and related subspace methods with scikit-learn compatible APIs. The crate emphasizes numerical robustness, GPU acceleration, and seamless integration with the broader sklears ecosystem.

Key Features

• Comprehensive Algorithms: LDA, QDA, shrinkage estimators, regularized discriminant analysis, and Bayesian variants.
• Performance Optimizations: SIMD-enabled linear algebra, batched matrix factorizations, and optional GPU backends.
• Pipeline Support: Works with sklears pipelines, calibration, and model selection utilities.
• Probability Calibration: Built-in support for Platt scaling and isotonic calibration for multiclass scenarios.

Quick Start

use sklears_discriminant_analysis::LinearDiscriminantAnalysis;
use scirs2_core::ndarray::{array, Array1};

let x = array![
    [1.0, 2.0],
    [1.5, 1.8],
    [5.0, 8.0],
    [6.0, 9.0],
];
let y = Array1::from(vec![0, 0, 1, 1]);

let lda = LinearDiscriminantAnalysis::builder()
    .solver("svd")
    .shrinkage(None)
    .n_components(Some(1))
    .build();

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

Status

• Covered by the shared 11,292 passing workspace tests in the 0.1.0-beta.1 release.
• Numerical stability validated on high-dimensional datasets using SciRS2 linear algebra backends.
• Future enhancements (incremental LDA, GPU QDA) tracked within this crate's TODO.md.