sklears-dummy

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

Overview

sklears-dummy implements baseline estimators for regression and classification, mirroring scikit-learn’s dummy module. These models provide sanity checks, benchmarking baselines, and quick data diagnostics.

Key Features

• Strategies: Mean, median, constant, stratified, most frequent, uniform, and custom priors.
• Compatibility: Works with classification, regression, multi-output, and probabilistic evaluation.
• Pipelines: Seamless integration with sklears pipelines, metrics, and inspection tooling.
• Diagnostics: Utilities for baseline comparisons and sanity checks during experimentation.

Quick Start

use sklears_dummy::DummyClassifier;
use scirs2_core::ndarray::{array, Array1};

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

let dummy = DummyClassifier::builder()
    .strategy("most_frequent")
    .random_state(Some(42))
    .build();

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

Status

• Included in the 11,292 passing workspace tests for 0.1.0-beta.1.
• Perfect for establishing baselines before deploying advanced models.
• Future enhancements (time-series baselines, streaming priors) logged in TODO.md.