Struct ExtraTreesRegressor 

pub struct ExtraTreesRegressor<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 random_state: Option<u64>,
    pub n_jobs: Option<usize>,
    /* private fields */
}

Extremely randomized trees regressor (ensemble).

Builds an ensemble of ExtraTreeRegressor base estimators, each using random split thresholds and random feature subsets at every node. Final predictions are the mean across all trees.

Unlike RandomForestRegressor, bootstrap sampling is disabled by default.

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.

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> ExtraTreesRegressor<F>

pub fn new() -> Self

Create a new ExtraTreesRegressor with default settings.

Defaults: n_estimators = 100, max_depth = None, max_features = All, min_samples_split = 2, min_samples_leaf = 1, bootstrap = false, 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_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 ExtraTreesRegressor<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for ExtraTreesRegressor<F>

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

Formats the value using the given formatter.

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

fn default() -> Self

Returns the “default value” for a type.

impl<'de, F> Deserialize<'de> for ExtraTreesRegressor<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<F>, Dim<[usize; 1]>>> for ExtraTreesRegressor<F>

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

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

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 = FittedExtraTreesRegressor<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F: Float + Send + Sync + 'static> PipelineEstimator<F> for ExtraTreesRegressor<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 ExtraTreesRegressor<F>

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

where __S: Serializer,

Serialize this value into the given Serde serializer.