Struct CostSensitiveBinaryRelevance 

pub struct CostSensitiveBinaryRelevance<S = Untrained> { /* private fields */ }

Cost-Sensitive Binary Relevance

Binary relevance approach that incorporates label-specific misclassification costs to optimize cost-sensitive performance rather than accuracy.

Implementations

impl CostSensitiveBinaryRelevance<Untrained>

pub fn new() -> Self

Create a new cost-sensitive binary relevance classifier

pub fn cost_matrix(self, cost_matrix: CostMatrix) -> Self

Set the cost matrix

pub fn learning_rate(self, learning_rate: Float) -> Self

Set the learning rate

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

Set the maximum number of iterations

pub fn regularization(self, regularization: Float) -> Self

Set the regularization strength

impl CostSensitiveBinaryRelevance<CostSensitiveBinaryRelevanceTrained>

pub fn cost_matrix(&self) -> &CostMatrix

Get the cost matrix

pub fn thresholds(&self) -> Vec<Float>

Get model thresholds

Trait Implementations

impl<S: Clone> Clone for CostSensitiveBinaryRelevance<S>

fn clone(&self) -> CostSensitiveBinaryRelevance<S>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<S: Debug> Debug for CostSensitiveBinaryRelevance<S>

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

Formats the value using the given formatter.

impl Default for CostSensitiveBinaryRelevance<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for CostSensitiveBinaryRelevance<Untrained>

type Config = ()

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<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 2]>>> for CostSensitiveBinaryRelevance<Untrained>

type Fitted = CostSensitiveBinaryRelevance<CostSensitiveBinaryRelevanceTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &Array2<i32>, ) -> 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<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 2]>>> for CostSensitiveBinaryRelevance<CostSensitiveBinaryRelevanceTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<i32>>

Make predictions on the provided data

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

Make predictions with confidence intervals