Struct ExtraTreesClassifier 

pub struct ExtraTreesClassifier {
    pub n_estimators: usize,
    pub max_depth: Option<usize>,
    pub min_samples_split: usize,
    pub min_samples_leaf: usize,
    pub max_features: Option<usize>,
    pub seed: u64,
}

Extra-Trees (Extremely Randomized Trees) classifier parameters (unfitted state).

Trains an ensemble of decision trees using random split thresholds instead of the best possible split at each node. Unlike Random Forests, Extra-Trees does not use bootstrap sampling — each tree is trained on the full dataset. However, each tree still considers a random subset of features at each split.

The randomization in split thresholds reduces variance further than Random Forests and can lead to smoother decision boundaries.

Fields

n_estimators: usize

Number of trees in the forest.

max_depth: Option<usize>

Maximum depth of each tree.

min_samples_split: usize

Minimum samples required to split a node.

min_samples_leaf: usize

Minimum samples required in a leaf node.

max_features: Option<usize>

Number of features to consider per tree. If None, all features are used.

seed: u64

Random seed for reproducibility.

Implementations

impl ExtraTreesClassifier

pub fn new(n_estimators: usize) -> Self

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

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

Set the maximum depth of each tree.

pub fn with_min_samples_split(self, min_samples_split: usize) -> Self

Set the minimum number of samples required to split a node.

pub fn with_min_samples_leaf(self, min_samples_leaf: usize) -> Self

Set the minimum number of samples required in a leaf node.

pub fn with_max_features(self, max_features: Option<usize>) -> Self

Set the number of features to consider per tree.

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

Set the random seed for reproducibility.

Trait Implementations

impl Clone for ExtraTreesClassifier

fn clone(&self) -> ExtraTreesClassifier

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ExtraTreesClassifier

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

Formats the value using the given formatter.

impl Default for ExtraTreesClassifier

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for ExtraTreesClassifier

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 ExtraTreesClassifier

type Fitted = FittedExtraTreesClassifier<F>

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

impl Serialize for ExtraTreesClassifier

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

where __S: Serializer,

Serialize this value into the given Serde serializer.