Struct BootstrapCV 

pub struct BootstrapCV { /* private fields */ }

Bootstrap cross-validator with confidence interval estimation

Bootstrap cross-validation uses sampling with replacement to create training sets of the same size as the original dataset. The out-of-bag (OOB) samples serve as the test set. This provides bootstrap estimates of model performance with built-in confidence intervals.

Implementations

impl BootstrapCV

pub fn new(n_splits: usize) -> Self

Create a new Bootstrap cross-validator

pub fn train_size(self, size: f64) -> Self

Set the size of the training set as a proportion (0.0 to 1.0) of the dataset

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

Set the random state for reproducible results

Trait Implementations

impl Clone for BootstrapCV

fn clone(&self) -> BootstrapCV

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl CrossValidator for BootstrapCV

fn n_splits(&self) -> usize

Returns the number of splits

fn split( &self, n_samples: usize, _y: Option<&Array1<i32>>, ) -> Vec<(Vec<usize>, Vec<usize>)>

Generate train/test indices for cross-validation

impl Debug for BootstrapCV

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.