Module: aprender::model_selection

Public module of the aprender-core crate.

Source

crates/aprender-core/src/model_selection.rs or directory.

Example

use aprender::model_selection::{train_test_split, cross_validate, KFold, StratifiedKFold};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::model_selection is the validation & tuning toolkit: KFold and StratifiedKFold for fold generation, train_test_split for one-shot holdout, cross_validate to run an Estimator over those folds, and grid_search_alpha for tuning regularization strength. The CrossValidationResult and GridSearchResult types pack per-fold scores plus aggregated mean / std / min / max accessors so you can build confidence intervals around your point estimates.

Key types

Usage patterns

Pattern 1: 5-fold cross-validation of a linear model

use aprender::model_selection::{cross_validate, KFold};
use aprender::prelude::*;

let x = Matrix::from_vec(10, 1, (1..=10).map(|i| i as f32).collect()).expect("10x1");
let y = Vector::from_slice(&[2.1, 4.0, 6.1, 8.0, 10.1, 11.9, 14.0, 16.1, 17.9, 20.0]);

let kfold = KFold::new(5).with_shuffle(true).with_random_state(42);
let model_factory = || LinearRegression::new();

let result = cross_validate(model_factory, &x, &y, &kfold)
    .expect("cv runs cleanly");
println!("cv R²: mean={:.4}  std={:.4}", result.mean(), result.std());

Pattern 2: Stratified split for classification

use aprender::model_selection::StratifiedKFold;
use aprender::primitives::Vector;

let y = Vector::from_slice(&[
    0.0, 0.0, 0.0, 0.0, 0.0,
    1.0, 1.0, 1.0, 1.0,
    2.0, 2.0,
]);

let skf = StratifiedKFold::new(3)
    .with_shuffle(true)
    .with_random_state(7);

let folds = skf.split(&y);
for (i, (train_idx, val_idx)) in folds.iter().enumerate() {
    println!("fold {}: train={}, val={}", i, train_idx.len(), val_idx.len());
}

See also

• metrics — the score functions invoked by cross_validate
• linear_model — grid_search_alpha is tailored for Ridge / Lasso / ElasticNet
• traits — cross_validate is generic over Estimator
• automl — higher-level automation that bundles model selection + tuning

Full API

Run cargo doc -p aprender-core --open for the rendered rustdoc, or browse docs.rs/aprender for the published version.