Struct GradientBoostingClassifier 

pub struct GradientBoostingClassifier {
    pub n_estimators: usize,
    pub learning_rate: f64,
    pub max_depth: Option<usize>,
    pub min_samples_split: usize,
    pub min_samples_leaf: usize,
    pub subsample: f64,
    pub seed: u64,
}

Gradient boosting classifier parameters (unfitted state).

For binary classification, fits trees to the negative gradient of the log loss (logistic regression loss). For multi-class (>2 classes), uses a one-vs-rest strategy with separate sets of trees for each class.

Fields

n_estimators: usize

Number of boosting rounds (trees per class for multi-class).

learning_rate: f64

Shrinkage applied to each tree’s contribution.

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.

subsample: f64

Fraction of training samples used per tree.

seed: u64

Random seed for reproducibility.

Implementations

impl GradientBoostingClassifier

pub fn new() -> Self

Create a new GradientBoostingClassifier with default parameters.

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

Set the number of boosting rounds.

pub fn with_learning_rate(self, learning_rate: f64) -> Self

Set the learning rate (shrinkage).

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_subsample(self, subsample: f64) -> Self

Set the fraction of samples used per boosting round.

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

Set the random seed for reproducibility.

Trait Implementations

impl Clone for GradientBoostingClassifier

fn clone(&self) -> GradientBoostingClassifier

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GradientBoostingClassifier

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

Formats the value using the given formatter.

impl Default for GradientBoostingClassifier

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GradientBoostingClassifier

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 GradientBoostingClassifier

type Fitted = FittedGradientBoostingClassifier<F>

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

impl Serialize for GradientBoostingClassifier

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

where __S: Serializer,

Serialize this value into the given Serde serializer.