Struct GradientBoostingSelector 

pub struct GradientBoostingSelector<State = Untrained> { /* private fields */ }

Gradient Boosting Feature Selection Uses gradient boosting to estimate feature importance and select features

Implementations

impl GradientBoostingSelector<Untrained>

pub fn new() -> Self

Create a new GradientBoostingSelector

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

Set the number of boosting estimators

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

Set the learning rate

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

Set the maximum depth of decision stumps

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

Set the minimum samples required to split a node

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

Set the minimum samples required at a leaf

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

Set the subsample ratio for training each estimator

pub fn threshold(self, threshold: Option<f64>) -> Self

Set the threshold for feature selection

pub fn max_features(self, max_features: Option<usize>) -> Self

Set the maximum number of features to select

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

Set the random state for reproducibility

impl GradientBoostingSelector<Trained>

pub fn feature_importances(&self) -> SklResult<&Array1<Float>>

Get feature importances

pub fn selected_features(&self) -> SklResult<&[usize]>

Get selected features

pub fn estimators(&self) -> SklResult<&[DecisionStump]>

Get the trained estimators (decision stumps)

Trait Implementations

impl<State: Clone> Clone for GradientBoostingSelector<State>

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for GradientBoostingSelector<State>

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

Formats the value using the given formatter.

impl Default for GradientBoostingSelector<Untrained>

fn default() -> Self

Returns the “default value” for a type.

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

type Fitted = GradientBoostingSelector<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &Array1<Float>) -> 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 SelectorMixin for GradientBoostingSelector<Trained>

fn get_support(&self) -> SklResult<Array1<bool>>

Get support mask

fn transform_features(&self, indices: &[usize]) -> SklResult<Vec<usize>>

Transform by selecting features

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

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

Transform the input data