Struct ExtraTreeRegressor 

pub struct ExtraTreeRegressor<F> {
    pub max_depth: Option<usize>,
    pub min_samples_split: usize,
    pub min_samples_leaf: usize,
    pub max_features: MaxFeatures,
    pub criterion: RegressionCriterion,
    pub random_state: Option<u64>,
    /* private fields */
}

Extremely randomized tree regressor.

Like a DecisionTreeRegressor, but split thresholds are chosen randomly rather than via exhaustive search. For each candidate feature, a random threshold is drawn uniformly between the feature’s minimum and maximum values in the current node.

Type Parameters

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

Fields

max_depth: Option<usize>

Maximum depth of the 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.

criterion: RegressionCriterion

Splitting criterion.

random_state: Option<u64>

Random seed for reproducibility. None means non-deterministic.

Implementations

impl<F: Float> ExtraTreeRegressor<F>

pub fn new() -> Self

Create a new ExtraTreeRegressor with default settings.

Defaults: max_depth = None, min_samples_split = 2, min_samples_leaf = 1, max_features = All, criterion = MSE, random_state = None.

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 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: MaxFeatures) -> Self

Set the maximum features strategy.

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

Set the splitting criterion.

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

Set the random seed for reproducibility.

Trait Implementations

impl<F: Clone> Clone for ExtraTreeRegressor<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for ExtraTreeRegressor<F>

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

Formats the value using the given formatter.

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

fn default() -> Self

Returns the “default value” for a type.

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

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

Fit the extra-tree regressor on the training data.

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 hyperparameters are invalid.

type Fitted = FittedExtraTreeRegressor<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 ExtraTreeRegressor<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 ExtraTreeRegressor<F>

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

where __S: Serializer,

Serialize this value into the given Serde serializer.