Struct AdaBoostClassifier 

pub struct AdaBoostClassifier<F> {
    pub n_estimators: usize,
    pub learning_rate: f64,
    pub algorithm: AdaBoostAlgorithm,
    pub random_state: Option<u64>,
    /* private fields */
}

AdaBoost classifier using decision tree stumps as base estimators.

At each boosting round a decision tree stump (max depth = 1) is fitted to the weighted training data. Misclassified samples receive higher weight in subsequent rounds, allowing the ensemble to focus on hard examples.

Type Parameters

• F: The floating-point type (f32 or f64).

Fields

n_estimators: usize

Number of boosting stages (stumps).

learning_rate: f64

Learning rate (shrinkage). Lower values require more estimators.

algorithm: AdaBoostAlgorithm

Algorithm variant (SAMME or SAMME.R).

random_state: Option<u64>

Random seed for reproducibility.

Implementations

impl<F: Float> AdaBoostClassifier<F>

pub fn new() -> Self

Create a new AdaBoostClassifier with default settings.

Defaults: n_estimators = 50, learning_rate = 1.0, algorithm = SAMME.R, random_state = None.

pub fn with_n_estimators(self, n: usize) -> Self

Set the number of boosting stages.

pub fn with_learning_rate(self, lr: f64) -> Self

Set the learning rate.

pub fn with_algorithm(self, algo: AdaBoostAlgorithm) -> Self

Set the algorithm variant.

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

Set the random seed for reproducibility.

Trait Implementations

impl<F: Clone> Clone for AdaBoostClassifier<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for AdaBoostClassifier<F>

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

Formats the value using the given formatter.

impl<F: Float> Default for AdaBoostClassifier<F>

fn default() -> Self

Returns the “default value” for a type.

impl<F: Float + Send + Sync + 'static> Fit<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>>> for AdaBoostClassifier<F>

fn fit( &self, x: &Array2<F>, y: &Array1<usize>, ) -> Result<FittedAdaBoostClassifier<F>, FerroError>

Fit the AdaBoost classifier.

Errors

Returns FerroError::ShapeMismatch if x and y have different numbers of samples. Returns FerroError::InsufficientSamples if there are no samples. Returns FerroError::InvalidParameter for invalid hyperparameters.

type Fitted = FittedAdaBoostClassifier<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F: Float + ToPrimitive + FromPrimitive + Send + Sync + 'static> PipelineEstimator<F> for AdaBoostClassifier<F>

fn fit_pipeline( &self, x: &Array2<F>, y: &Array1<F>, ) -> Result<Box<dyn FittedPipelineEstimator<F>>, FerroError>

Fit this estimator on the given data.