Struct SelectKBest 

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

Select the top-k highest-scoring features.

Uses a scoring function (e.g. ANOVA F-value) to rank features by their discriminative power, then keeps only the k best.

Examples

use scry_learn::prelude::*;

let mut sel = SelectKBest::new(ScoreFn::FClassif).k(2);
sel.fit(&data)?;
sel.transform(&mut data)?;
assert_eq!(data.n_features(), 2);

Implementations

impl SelectKBest

pub fn new(score_fn: ScoreFn) -> Self

Create a new selector with the given scoring function.

Default: keep top 10 features.

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

Set the number of top features to keep.

pub fn scores(&self) -> &[f64]

Per-feature scores computed during fit.

Higher values indicate more discriminative features.

pub fn get_support(&self) -> &[bool]

Boolean mask of selected features.

true at index j means feature j was kept.

Trait Implementations

impl Clone for SelectKBest

fn clone(&self) -> SelectKBest

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for SelectKBest

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

Formats the value using the given formatter.

impl Transformer for SelectKBest

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

Learn parameters from the training data.

fn transform(&self, data: &mut Dataset) -> Result<()>

Apply the learned transformation to a dataset (in-place).

fn inverse_transform(&self, _data: &mut Dataset) -> Result<()>

Reverse the transformation (if invertible).

fn fit_transform(&mut self, data: &mut Dataset) -> Result<()>

Convenience: fit + transform in one call.