Struct HistGradientBoostingRegressor 

pub struct HistGradientBoostingRegressor {
    pub n_estimators: usize,
    pub learning_rate: f64,
    pub max_depth: usize,
    pub min_samples_leaf: usize,
    pub l2_regularization: f64,
    pub max_bins: usize,
}

Histogram-based gradient boosting regressor.

Much faster than GradientBoostingRegressor for datasets with >1000 samples. Features are discretized into 256 bins for O(n) split finding.

Fields

n_estimators: usize

learning_rate: f64

max_depth: usize

min_samples_leaf: usize

l2_regularization: f64

max_bins: usize

Implementations

impl HistGradientBoostingRegressor

pub fn new() -> Self

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

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

pub fn with_max_depth(self, d: usize) -> Self

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

pub fn with_l2_regularization(self, l2: f64) -> Self

pub fn with_max_bins(self, b: usize) -> Self

Trait Implementations

impl Clone for HistGradientBoostingRegressor

fn clone(&self) -> HistGradientBoostingRegressor

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for HistGradientBoostingRegressor

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

Formats the value using the given formatter.

impl Default for HistGradientBoostingRegressor

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for HistGradientBoostingRegressor

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Fit<f64> for HistGradientBoostingRegressor

type Fitted = FittedHistGradientBoostingRegressor

fn fit(&self, x: &Array2<f64>, y: &Array1<f64>) -> Result<Self::Fitted>

impl Serialize for HistGradientBoostingRegressor

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

where __S: Serializer,

Serialize this value into the given Serde serializer.