Struct MonteCarloCV 

pub struct MonteCarloCV { /* private fields */ }

Monte Carlo Cross-Validation with random subsampling

Monte Carlo CV repeatedly randomly splits the data into training and test sets, unlike K-fold CV which ensures each sample appears exactly once in a test set. This allows for more flexible control over train/test sizes and provides bootstrap-like estimates of model performance.

Implementations

impl MonteCarloCV

pub fn new(n_splits: usize, test_size: f64) -> Self

Create a new Monte Carlo cross-validator

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

Set the training set size 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 MonteCarloCV

fn clone(&self) -> MonteCarloCV

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl CrossValidator for MonteCarloCV

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 MonteCarloCV

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

Formats the value using the given formatter.