Struct BaggingClassifier 

pub struct BaggingClassifier {
    pub n_estimators: usize,
    pub max_depth: Option<usize>,
    pub max_samples: Option<f64>,
    pub bootstrap: bool,
    pub seed: u64,
}

Bagging (Bootstrap Aggregating) classifier parameters (unfitted state).

Trains an ensemble of decision tree classifiers, each on a bootstrap sample of the data using the full feature set. Unlike RandomForestClassifier, bagging does not perform random feature subsampling at the tree level – every tree sees all features.

Predictions are made by majority vote across all trees.

Fields

n_estimators: usize

Number of trees in the ensemble.

max_depth: Option<usize>

Maximum depth of each tree.

max_samples: Option<f64>

Fraction of samples to draw for each tree (with replacement when bootstrap=true). If None, draws n_samples. Value in (0, 1].

bootstrap: bool

Whether to use bootstrap sampling. Default: true.

seed: u64

Random seed for reproducibility.

Implementations

impl BaggingClassifier

pub fn new(n_estimators: usize) -> Self

Create a new BaggingClassifier with the given number of trees and default parameters.

pub fn with_max_depth(self, max_depth: Option<usize>) -> Self

pub fn with_max_samples(self, max_samples: Option<f64>) -> Self

pub fn with_bootstrap(self, bootstrap: bool) -> Self

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

Trait Implementations

impl Clone for BaggingClassifier

fn clone(&self) -> BaggingClassifier

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for BaggingClassifier

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

Formats the value using the given formatter.

impl Default for BaggingClassifier

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for BaggingClassifier

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F: Float> Fit<F> for BaggingClassifier

type Fitted = FittedBaggingClassifier<F>

fn fit(&self, x: &Array2<F>, y: &Array1<F>) -> Result<Self::Fitted>

impl Serialize for BaggingClassifier

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.