Struct RandomForestClassifier 

pub struct RandomForestClassifier {
    pub n_estimators: usize,
    pub max_depth: Option<usize>,
    pub min_samples_split: usize,
    pub min_samples_leaf: usize,
    pub max_features: Option<usize>,
    pub criterion: SplitCriterion,
    pub bootstrap: bool,
    pub max_samples: Option<f64>,
    pub class_weight: Option<ClassWeight>,
    pub oob_score: bool,
    pub seed: u64,
}

Random forest classifier parameters (unfitted state).

Trains an ensemble of decision tree classifiers, each on a bootstrap sample of the data with an optional random subset of features.

Fields

n_estimators: usize

Number of trees in the forest.

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.

max_features: Option<usize>

Number of features to consider per tree. If None, all features are used.

criterion: SplitCriterion

Split criterion for each tree. Default: Gini.

bootstrap: bool

Whether to use bootstrap sampling. Default: true.

max_samples: Option<f64>

Fraction of samples to draw for each tree (when bootstrap=true). If None, draws n_samples (with replacement). Value in (0, 1].

class_weight: Option<ClassWeight>

Class weight strategy passed to each tree.

oob_score: bool

Whether to compute out-of-bag score after fitting.

seed: u64

Random seed for reproducibility.

Implementations

impl RandomForestClassifier

pub fn new(n_estimators: usize) -> Self

Create a new RandomForestClassifier with the given number of trees and default parameters.

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

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

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

pub fn with_max_features(self, max_features: Option<usize>) -> Self

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

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

pub fn with_max_samples(self, max_samples: Option<f64>) -> Self

pub fn with_class_weight(self, class_weight: Option<ClassWeight>) -> Self

pub fn with_oob_score(self, oob_score: bool) -> Self

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

Trait Implementations

impl Clone for RandomForestClassifier

fn clone(&self) -> RandomForestClassifier

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for RandomForestClassifier

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

Formats the value using the given formatter.

impl Default for RandomForestClassifier

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for RandomForestClassifier

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 RandomForestClassifier

type Fitted = FittedRandomForestClassifier<F>

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

impl Serialize for RandomForestClassifier

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

where __S: Serializer,

Serialize this value into the given Serde serializer.