Struct VotingClassifier 

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

VotingClassifier

Ensemble classifier that combines multiple classifiers using majority voting or probability averaging.

Type Parameters

• S - State type (Untrained or VotingClassifierTrained)

Examples

use sklears_compose::{VotingClassifier, MockPredictor};
use scirs2_core::ndarray::array;

let voting_clf = VotingClassifier::builder()
    .estimator("clf1", Box::new(MockPredictor::new()))
    .estimator("clf2", Box::new(MockPredictor::new()))
    .voting("hard")
    .build();

Implementations

impl VotingClassifier<Untrained>

pub fn new() -> Self

Create a new VotingClassifier instance

pub fn builder() -> VotingClassifierBuilder

Create a voting classifier builder

pub fn add_estimator( &mut self, name: String, estimator: Box<dyn PipelinePredictor>, )

Add an estimator

pub fn voting(self, voting: &str) -> Self

Set voting strategy

pub fn weights(self, weights: Vec<f64>) -> Self

Set weights

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

Set number of jobs

pub fn flatten_transform(self, flatten: bool) -> Self

Set flatten transform

Trait Implementations

impl Default for VotingClassifier<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for VotingClassifier<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]>>, Option<&ArrayBase<ViewRepr<&f64>, Dim<[usize; 1]>>>> for VotingClassifier<Untrained>

type Fitted = VotingClassifier<VotingClassifierTrained>

The fitted model type

fn fit( self, x: &ArrayView2<'_, Float>, y: &Option<&ArrayView1<'_, Float>>, ) -> 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