Struct ExtraTreesClassifier 

pub struct ExtraTreesClassifier<F> {
    pub n_estimators: usize,
    pub max_depth: Option<usize>,
    pub min_samples_split: usize,
    pub min_samples_leaf: usize,
    pub max_features: MaxFeatures,
    pub bootstrap: bool,
    pub criterion: ClassificationCriterion,
    pub random_state: Option<u64>,
    pub n_jobs: Option<usize>,
    /* private fields */
}

Extremely randomized trees classifier (ensemble).

Builds an ensemble of ExtraTreeClassifier base estimators, each using random split thresholds and random feature subsets at every node. Final predictions are made by majority vote.

Unlike RandomForestClassifier, bootstrap sampling is disabled by default. Randomness comes from the random thresholds and random feature subsets at each split.

Type Parameters

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

Fields

n_estimators: usize

Number of trees in the ensemble.

max_depth: Option<usize>

Maximum depth of each tree. None means unlimited.

min_samples_split: usize

Minimum number of samples required to split an internal node.

min_samples_leaf: usize

Minimum number of samples required in a leaf node.

max_features: MaxFeatures

Strategy for the number of features considered at each split.

bootstrap: bool

Whether to use bootstrap sampling. Default is false.

criterion: ClassificationCriterion

Splitting criterion.

random_state: Option<u64>

Random seed for reproducibility. None means non-deterministic.

n_jobs: Option<usize>

Number of parallel jobs. None means use all available cores.

Implementations

impl<F: Float> ExtraTreesClassifier<F>

pub fn new() -> Self

Create a new ExtraTreesClassifier with default settings.

Defaults: n_estimators = 100, max_depth = None, max_features = Sqrt, min_samples_split = 2, min_samples_leaf = 1, bootstrap = false, criterion = Gini, random_state = None, n_jobs = None.

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

Set the number of trees.

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

Set the maximum tree depth.

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

Set the minimum number of samples to split a node.

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

Set the minimum number of samples in a leaf.

pub fn with_max_features(self, max_features: MaxFeatures) -> Self

Set the maximum features strategy.

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

Set whether to use bootstrap sampling.

pub fn with_criterion(self, criterion: ClassificationCriterion) -> Self

Set the splitting criterion.

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

Set the random seed for reproducibility.

pub fn with_n_jobs(self, n_jobs: usize) -> Self

Set the number of parallel jobs.

Trait Implementations

impl<F: Clone> Clone for ExtraTreesClassifier<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for ExtraTreesClassifier<F>

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

Formats the value using the given formatter.

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

fn default() -> Self

Returns the “default value” for a type.

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

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

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

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

Fit the ensemble by building n_estimators extra-trees in parallel.

Each tree uses random split thresholds and random feature subsets at every node. If bootstrap is true, each tree is trained on a bootstrap sample; otherwise all samples are used.

Errors

Returns FerroError::ShapeMismatch if x and y have different numbers of samples. Returns FerroError::InsufficientSamples if there are no samples. Returns FerroError::InvalidParameter if n_estimators is 0.

type Fitted = FittedExtraTreesClassifier<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 ExtraTreesClassifier<F>

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

Fit this estimator on the given data.

impl<F> Serialize for ExtraTreesClassifier<F>

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

where __S: Serializer,

Serialize this value into the given Serde serializer.