Struct SequentialFeatureSelection 

pub struct SequentialFeatureSelection { /* private fields */ }

Sequential Feature Selection for discriminant analysis

Implementations

impl SequentialFeatureSelection

pub fn new() -> Self

Create a new Sequential Feature Selection model

pub fn direction(self, direction: SelectionDirection) -> Self

Set the selection direction

pub fn n_features_to_select(self, n_features: usize) -> Self

Set the number of features to select

pub fn n_features_fraction(self, fraction: Float) -> Self

Set the fraction of features to select

pub fn scoring(self, scoring: &str) -> Self

Set the scoring method

pub fn cv(self, cv_folds: usize) -> Self

Set the number of cross-validation folds

pub fn tol(self, tol: Float) -> Self

Set the tolerance for improvement

pub fn estimator_config(self, config: LinearDiscriminantAnalysisConfig) -> Self

Set the base estimator configuration

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

Set random state

pub fn verbose(self, verbose: bool) -> Self

Set verbose output

Trait Implementations

impl Clone for SequentialFeatureSelection

fn clone(&self) -> SequentialFeatureSelection

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for SequentialFeatureSelection

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

Formats the value using the given formatter.

impl Default for SequentialFeatureSelection

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for SequentialFeatureSelection

type Config = SequentialFeatureSelectionConfig

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>>> for SequentialFeatureSelection

type Fitted = TrainedSequentialFeatureSelection

The fitted model type

fn fit( self, x: &Array2<Float>, y: &Array1<i32>, ) -> Result<TrainedSequentialFeatureSelection>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping