Struct RFECV 

pub struct RFECV<E, State = Untrained> { /* private fields */ }

Recursive Feature Elimination with Cross-Validation

Implementations

impl<E: Clone> RFECV<E, Untrained>

pub fn new(estimator: E) -> Self

Create a new RFECV selector

pub fn min_features_to_select( self, min_features: usize, ) -> Result<Self, SklearsError>

Set the minimum number of features to consider

pub fn step(self, step: f64) -> Result<Self, SklearsError>

Set the step size for feature elimination

pub fn cv(self, cv: Box<dyn CrossValidator>) -> Self

Set the cross-validation strategy

impl<E> RFECV<E, Trained>

pub fn support(&self) -> SklResult<&Array1<bool>>

Get the support mask

pub fn ranking(&self) -> SklResult<&Array1<usize>>

Get the feature ranking

pub fn cv_results(&self) -> SklResult<&RFECVResults>

Get cross-validation results

Trait Implementations

impl<E> Estimator for RFECV<E, Untrained>

type Config = ()

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<E> Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>> for RFECV<E, Untrained>

where E: Clone + Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>> + Send + Sync, E::Fitted: FeatureImportance + Score<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>, Float = f64> + Send + Sync, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>: IndexableTarget,

type Fitted = RFECV<E, Trained>

The fitted model type

fn fit( self, x: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, y: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>, ) -> SklResult<Self::Fitted>

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

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

where E: Clone + Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>, i32>> + Send + Sync, E::Fitted: FeatureImportance + Score<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>, i32>, Float = f64> + Send + Sync, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>, i32>: IndexableTarget,

type Fitted = RFECV<E, Trained>

The fitted model type

fn fit( self, x: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, y: &ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>, i32>, ) -> SklResult<Self::Fitted>

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

impl<E> Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>, usize>> for RFECV<E, Untrained>

where E: Clone + Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>, usize>> + Send + Sync, E::Fitted: FeatureImportance + Score<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>, usize>, Float = f64> + Send + Sync, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>, usize>: IndexableTarget,

type Fitted = RFECV<E, Trained>

The fitted model type

fn fit( self, x: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, y: &ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>, usize>, ) -> SklResult<Self::Fitted>

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

impl<E> Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for RFECV<E, Trained>

fn transform(&self, x: &Array2<Float>) -> SklResult<Array2<Float>>

Transform the input data