Struct GridSearchCV 

#[non_exhaustive]
pub struct GridSearchCV { /* private fields */ }

Exhaustive search over a hyperparameter grid with cross-validation.

Tries every combination in the grid, evaluates each with k-fold CV, and reports the best-performing parameter set.

Examples

use scry_learn::prelude::*;
use scry_learn::search::*;

let mut grid = ParamGrid::new();
grid.insert("max_depth".into(), vec![
    ParamValue::Int(2), ParamValue::Int(4), ParamValue::Int(8),
]);

let result = GridSearchCV::new(DecisionTreeClassifier::new(), grid)
    .cv(5)
    .scoring(accuracy)
    .fit(&data)
    .unwrap();

println!("Best: {:?} → {:.3}", result.best_params(), result.best_score());

Implementations

impl GridSearchCV

pub fn new(model: impl Tunable + 'static, grid: ParamGrid) -> Self

Create a grid search over the given model and parameter grid.

Defaults: 5-fold CV, accuracy scorer, seed 42, non-stratified.

pub fn cv(self, k: usize) -> Self

Set the number of cross-validation folds (default: 5).

pub fn scoring(self, scorer: ScoringFn) -> Self

Set the scoring function (default: accuracy).

pub fn seed(self, seed: u64) -> Self

Set the random seed for fold generation (default: 42).

pub fn stratified(self, stratified: bool) -> Self

Enable stratified k-fold CV (default: false).

When true, uses stratified_k_fold to preserve class proportions in each fold.

pub fn fit(self, data: &Dataset) -> Result<Self>

Run the exhaustive grid search.

Returns self for chained accessor calls.

pub fn best_params(&self) -> &HashMap<String, ParamValue>

The best parameter combination found.

Panics

Panics if called before fit.

pub fn best_score(&self) -> f64

The best mean CV score achieved.

pub fn cv_results(&self) -> &[CvResult]

All evaluated combinations with their scores.