Struct RandomizedSearchCV 

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

Randomized search over a hyperparameter grid with cross-validation.

Samples n_iter random combinations from the grid instead of trying every one — much faster for large grids.

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(6), ParamValue::Int(8),
]);

let result = RandomizedSearchCV::new(DecisionTreeClassifier::new(), grid)
    .n_iter(5)
    .cv(3)
    .fit(&data)
    .unwrap();

Implementations

impl RandomizedSearchCV

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

Create a randomized search with n_iter random samples.

Defaults: 10 iterations, 5-fold CV, accuracy scorer, seed 42, non-stratified.

pub fn n_iter(self, n: usize) -> Self

Set the number of random combinations to try (default: 10).

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 (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 randomized search.

Samples up to n_iter random parameter combinations from the grid.

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.