Struct BlendingClassifier 

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

Blending Classifier

Uses a holdout validation approach to train base estimators on part of the data and generate meta-features on the remaining data for training a meta-learner.

Implementations

impl BlendingClassifier<Untrained>

pub fn new(n_base_estimators: usize) -> Self

Create a new blending classifier

pub fn holdout_ratio(self, ratio: f64) -> Self

Set the holdout ratio for validation set

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

Set the random state for reproducibility

impl BlendingClassifier<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 holdout_ratio(&self) -> f64

Get the holdout ratio used for validation

pub fn random_state(&self) -> Option<u64>

Get the random state used for reproducibility

Trait Implementations

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

type Fitted = BlendingClassifier<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 BlendingClassifier<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