Struct KFold 

pub struct KFold { /* private fields */ }

Plain K-fold cross-validation.

The sample indices are partitioned into k folds; each fold serves as the test set once while the remaining k - 1 folds form the training set. With shuffle the indices are permuted (deterministically, from the given seed) before partitioning.

This is a from-scratch implementation, so the crate has zero required dependency on smartcore; it is functionally equivalent to smartcore::model_selection::KFold for anyone who would rather use that.

use model_selection_rs::splitters::{CvSplitter, KFold};

let kf = KFold::new(5).unwrap().with_shuffle(42);
let splits = kf.split(50).unwrap();
assert_eq!(splits.len(), 5);

Implementations

impl KFold

pub fn new(n_splits: usize) -> Result<Self>

Create a KFold with n_splits folds and no shuffling.

Errors

Returns ModelSelectionError::InvalidSplitCount if n_splits < 2.

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

Enable shuffling of the sample order before folding, using seed.

Trait Implementations

impl Clone for KFold

fn clone(&self) -> KFold

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl CvSplitter for KFold

fn split(&self, n_samples: usize) -> Result<Vec<(Vec<usize>, Vec<usize>)>>

Produce every (train_indices, test_indices) pair for n_samples rows.

fn n_splits(&self) -> usize

Number of splits this strategy yields.

impl Debug for KFold

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

Formats the value using the given formatter.