Struct MultiLayerStackingClassifier 

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

Multi-Layer Stacking Classifier

Implements deep stacking with multiple layers, advanced meta-learning strategies, and ensemble selection techniques for improved prediction accuracy.

Implementations

impl MultiLayerStackingClassifier<Untrained>

pub fn new(config: MultiLayerStackingConfig) -> Self

Create a new multi-layer stacking classifier

pub fn two_layer(base_estimators: usize, meta_estimators: usize) -> Self

Create a simple two-layer stacking classifier

pub fn deep(n_layers: usize, estimators_per_layer: usize) -> Self

Create a deep stacking classifier with multiple layers

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

Set random state for reproducibility

impl MultiLayerStackingClassifier<Trained>

pub fn predict_proba(&self, x: &Array2<Float>) -> Result<Array2<Float>>

Predict class probabilities using the trained multi-layer stacking model

pub fn get_layer_feature_importances(&self) -> Option<&Vec<Array1<Float>>>

Get feature importances for each layer

pub fn get_diversity_scores(&self) -> Option<&Array1<Float>>

Get diversity scores for the final layer

pub fn n_features_in(&self) -> usize

Get the number of input features

pub fn classes(&self) -> &Array1<i32>

Get the classes

pub fn config(&self) -> &MultiLayerStackingConfig

Get the configuration

pub fn layers(&self) -> Option<&Vec<StackingLayer>>

Get the layers

Trait Implementations

impl<State: Debug> Debug for MultiLayerStackingClassifier<State>

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

Formats the value using the given formatter.

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

type Fitted = MultiLayerStackingClassifier<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &Array1<i32>) -> Result<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 Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>>> for MultiLayerStackingClassifier<Trained>

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

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals