Struct SimpleStackingClassifier 

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

Enhanced Stacking Classifier with working implementation

This implementation uses a holdout validation approach and simulates base estimators with simple linear predictors for demonstration. It provides a working stacking framework that can be extended.

Implementations

impl SimpleStackingClassifier<Untrained>

pub fn new(n_base_estimators: usize) -> Self

Create a new simple stacking classifier

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

Set the number of cross-validation folds

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

Set whether to use probabilities

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

Set the random state for reproducibility

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

Set passthrough to include original features

impl SimpleStackingClassifier<Trained>

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

Get the classes

pub fn n_features_in(&self) -> usize

Get the number of features in the training data

pub fn n_base_estimators(&self) -> usize

Get the number of base estimators

pub fn base_weights(&self) -> &Array2<Float>

Get the base estimator weights

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

Get the base estimator intercepts

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

Get the meta-learner weights

pub fn meta_intercept(&self) -> Float

Get the meta-learner intercept

Trait Implementations

impl<State: Debug> Debug for SimpleStackingClassifier<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 SimpleStackingClassifier<Untrained>

type Fitted = SimpleStackingClassifier<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 SimpleStackingClassifier<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