Struct MultiLabelEnsembleClassifier 

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

Multi-label ensemble classifier

Implementations

impl MultiLabelEnsembleClassifier<Untrained>

pub fn new(config: MultiLabelEnsembleConfig) -> Self

Create a new multi-label ensemble classifier

pub fn binary_relevance() -> Self

Create a new multi-label ensemble classifier with binary relevance

pub fn label_powerset() -> Self

Create a new multi-label ensemble classifier with label powerset

pub fn classifier_chains() -> Self

Create a new multi-label ensemble classifier with classifier chains

pub fn ensemble_classifier_chains() -> Self

Create a new multi-label ensemble classifier with ensemble of classifier chains

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

Builder method to configure the number of estimators

pub fn aggregation_method(self, method: MultiLabelAggregationMethod) -> Self

Builder method to configure the aggregation method

pub fn correlation_method(self, method: LabelCorrelationMethod) -> Self

Builder method to configure the correlation method

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

Builder method to configure the threshold

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

Builder method to configure random state

pub fn prune_labelsets(self, prune: bool) -> Self

Builder method to configure label powerset pruning

impl MultiLabelEnsembleClassifier<Trained>

pub fn n_labels(&self) -> usize

Get the number of labels

pub fn label_correlations(&self) -> &Array2<f64>

Get label correlations

pub fn transformation_strategy(&self) -> &LabelTransformationStrategy

Get the transformation strategy used

pub fn predict_binary(&self, X: &Array2<f64>) -> SklResult<Array2<usize>>

Predict binary labels only

pub fn predict_proba(&self, X: &Array2<f64>) -> SklResult<Array2<f64>>

Predict probabilities only

pub fn predict_rankings(&self, X: &Array2<f64>) -> SklResult<Array2<f64>>

Get label rankings

Trait Implementations

impl Estimator for MultiLabelEnsembleClassifier<Untrained>

type Config = MultiLabelEnsembleConfig

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<usize>, Dim<[usize; 2]>>> for MultiLabelEnsembleClassifier<Untrained>

type Fitted = MultiLabelEnsembleClassifier<Trained>

The fitted model type

fn fit(self, X: &Array2<f64>, y: &Array2<usize>) -> 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 Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, MultiLabelPredictionResults> for MultiLabelEnsembleClassifier<Trained>

fn predict(&self, X: &Array2<f64>) -> SklResult<MultiLabelPredictionResults>

Make predictions on the provided data

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

Make predictions with confidence intervals