Struct AdaBoostClassifier 

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

AdaBoost Classifier

AdaBoost is a meta-algorithm that can be used in conjunction with many other types of learning algorithms to improve performance. The key idea is to fit a sequence of weak learners on repeatedly modified versions of the data.

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 boosting iterations

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

Set the learning rate

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

Set the random state for reproducible results

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

Set the algorithm variant

pub fn with_samme_r(self) -> Self

Use the SAMME.R algorithm variant

pub fn with_gentle(self) -> Self

Use the Gentle AdaBoost algorithm variant

impl AdaBoostClassifier<Trained>

pub fn estimators(&self) -> &[DecisionTreeClassifier<Trained>]

Get the fitted base estimators

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

Get the weights for each estimator

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

Get the errors for each estimator

pub fn classes(&self) -> &Array1<Float>

Get the classes

pub fn n_classes(&self) -> usize

Get the number of classes

pub fn n_features_in(&self) -> usize

Get the number of input features

pub fn feature_importances(&self) -> Result<Array1<Float>>

Get feature importances (averaged from all estimators)

pub fn predict_proba(&self, x: &Array2<Float>) -> Result<Array2<Float>>

Predict class probabilities using weighted voting

pub fn decision_function(&self, x: &Array2<Float>) -> Result<Array2<Float>>

Get decision function values

Trait Implementations

impl<State: Clone> Clone for AdaBoostClassifier<State>

fn clone(&self) -> AdaBoostClassifier<State>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State> Debug for AdaBoostClassifier<State>

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

Formats the value using the given formatter.

impl Default for AdaBoostClassifier<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for AdaBoostClassifier<Untrained>

type Fitted = AdaBoostClassifier<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &Array1<Float>) -> Result<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]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for AdaBoostClassifier<Trained>

fn predict(&self, x: &Array2<Float>) -> Result<Array1<Float>>

Make predictions on the provided data

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

Make predictions with confidence intervals