Struct AdaBoostClassifier 

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

AdaBoost classifier implementation

Implementations

impl AdaBoostClassifier<Untrained>

pub fn new() -> Self

Create a new AdaBoost classifier

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

Set the number of estimators

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

Set the learning rate

pub fn algorithm(self, algorithm: AdaBoostAlgorithm) -> Self

Set the algorithm

pub fn base_estimator(self, estimator: Box<dyn PipelinePredictor>) -> Self

Add a base estimator

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

Set random state

impl AdaBoostClassifier<AdaBoostTrained>

pub fn predict(&self, x: &ArrayView2<'_, Float>) -> SklResult<Array1<f64>>

Predict using the fitted AdaBoost ensemble

pub fn estimators(&self) -> &[Box<dyn PipelinePredictor>]

Get the fitted estimators

pub fn estimator_weights(&self) -> &Array1<f64>

Get the estimator weights

pub fn estimator_errors(&self) -> &Array1<f64>

Get the estimator errors

Trait Implementations

impl Default for AdaBoostClassifier<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for AdaBoostClassifier<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 AdaBoostClassifier<Untrained>

type Fitted = AdaBoostClassifier<AdaBoostTrained>

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