Struct BaggingRegressor 

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

Enhanced Bagging regressor with OOB estimation and feature bagging

Implementations

impl BaggingRegressor<Untrained>

pub fn new() -> Self

Create a new bagging regressor

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

Set the number of estimators

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

Set the random state

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

Set whether to calculate out-of-bag score

impl BaggingRegressor<Trained>

pub fn oob_score(&self) -> Option<Float>

Get the out-of-bag score if calculated

pub fn n_features_in(&self) -> usize

Get the number of input features

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

Get feature importances

Trait Implementations

impl Default for BaggingRegressor<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl<State> Estimator<State> for BaggingRegressor<State>

type Config = BaggingConfig

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<()>

Validate estimator configuration with detailed error context

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

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

Check if estimator is compatible with given data dimensions