Struct OneVsRestClassifier 

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

One-vs-Rest Classifier

A multi-label classification strategy that treats each label as a separate binary classification problem using a one-vs-rest approach. This is essentially the same as BinaryRelevance but with explicit one-vs-rest semantics.

Examples

use sklears_multioutput::OneVsRestClassifier;
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;

// This is a simple example showing the structure
let data = array![[1.0, 2.0], [2.0, 3.0], [3.0, 1.0]];
let labels = array![[1, 0], [0, 1], [1, 1]];

Implementations

impl OneVsRestClassifier<Untrained>

pub fn new() -> Self

Create a new OneVsRestClassifier instance

pub fn n_jobs(self, n_jobs: Option<i32>) -> Self

Set the number of parallel jobs

impl OneVsRestClassifier<OneVsRestClassifierTrained>

pub fn classes(&self) -> &[Vec<i32>]

Get the classes for each label

pub fn n_labels(&self) -> usize

Get the number of labels

impl OneVsRestClassifier<OneVsRestClassifierTrained>

pub fn predict_proba(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<f64>>

Predict class probabilities for each label

pub fn decision_function( &self, X: &ArrayView2<'_, Float>, ) -> SklResult<Array2<f64>>

Get decision scores for each label

Trait Implementations

impl<S: Clone> Clone for OneVsRestClassifier<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for OneVsRestClassifier<S>

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

Formats the value using the given formatter.

impl Default for OneVsRestClassifier<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for OneVsRestClassifier<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 OneVsRestClassifier<Untrained>

type Fitted = OneVsRestClassifier<OneVsRestClassifierTrained>

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 OneVsRestClassifier<OneVsRestClassifierTrained>

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