Struct AdaBoostValidParams 

pub struct AdaBoostValidParams<P, R> {
    pub n_estimators: usize,
    pub learning_rate: f64,
    pub model_params: P,
    pub rng: R,
}

The set of valid hyperparameters for the AdaBoost algorithm.

Parameters

• n_estimators: The maximum number of weak learners to train sequentially. More estimators generally improve performance but increase training time and risk overfitting. Typical values range from 50 to 500. Default: 50.

• learning_rate: Shrinks the contribution of each classifier. There is a trade-off between learning_rate and n_estimators. Lower values require more estimators to achieve the same performance but may generalize better. Must be positive. Default: 1.0.

• model_params: The parameters for the base learner (weak classifier). Typically, shallow decision trees (stumps with max_depth=1 or max_depth=2) are used as weak learners.

• rng: Random number generator used for bootstrap sampling and reproducibility.

Fields

n_estimators: usize

The maximum number of estimators to train

learning_rate: f64

The learning rate (shrinkage parameter)

model_params: P

The base learner parameters

rng: R

Random number generator

Trait Implementations

impl<P: Clone, R: Clone> Clone for AdaBoostValidParams<P, R>

fn clone(&self) -> AdaBoostValidParams<P, R>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<P: Copy, R: Copy> Copy for AdaBoostValidParams<P, R>

impl<P: Debug, R: Debug> Debug for AdaBoostValidParams<P, R>

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

Formats the value using the given formatter.

impl<D, T, P, R> Fit<ArrayBase<OwnedRepr<D>, Dim<[usize; 2]>>, T, Error> for AdaBoostValidParams<P, R>

where D: Clone + ScalarOperand, T: FromTargetArrayOwned<Owned = T> + AsTargets + Clone, T::Elem: Copy + Eq + Hash + Debug + Into<usize>, P: Fit<Array2<D>, T, Error> + Clone, P::Object: PredictInplace<Array2<D>, T>, R: Rng + Clone, usize: Into<T::Elem>,

type Object = AdaBoost<<P as Fit<ArrayBase<OwnedRepr<D>, Dim<[usize; 2]>>, T, Error>>::Object, <T as AsTargets>::Elem>

fn fit( &self, dataset: &DatasetBase<Array2<D>, T>, ) -> Result<Self::Object, Error>

impl<P: PartialEq, R: PartialEq> PartialEq for AdaBoostValidParams<P, R>

fn eq(&self, other: &AdaBoostValidParams<P, R>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<P, R> StructuralPartialEq for AdaBoostValidParams<P, R>