Struct RandomForestClassifier 

#[non_exhaustive]
pub struct RandomForestClassifier { /* private fields */ }

Random Forest for classification.

Trains an ensemble of decision trees in parallel, each on a bootstrap sample with a random subset of features. Predictions are by majority vote.

Implementations

impl RandomForestClassifier

pub fn new() -> Self

Create a new random forest with default parameters.

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

Set number of trees.

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

Set maximum depth per tree.

pub fn max_features(self, mf: MaxFeatures) -> Self

Set feature selection strategy.

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

Set minimum samples to split.

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

Set minimum samples per leaf.

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

Enable/disable bootstrap sampling.

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

Set the random seed.

pub fn class_weight(self, cw: ClassWeight) -> Self

Set class weighting strategy for imbalanced datasets.

pub fn fit(&mut self, data: &Dataset) -> Result<()>

Train the random forest.

OOB votes are accumulated into a shared atomic array during parallel build, avoiding retention of per-tree vote arrays or bootstrap indices. Dataset indices are pre-sorted once and shared across all trees to avoid per-tree sorted_by_feature allocation (~6 MB savings with 16 threads).

pub fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict class labels by majority vote.

Uses FlatTree::predict_sample for cache-optimal traversal. Parallelized across samples via rayon.

pub fn predict_proba(&self, features: &[Vec<f64>]) -> Result<Vec<Vec<f64>>>

Predict class probabilities (average across trees).

Parallelized across samples via rayon.

pub fn feature_importances(&self) -> Result<Vec<f64>>

Feature importances averaged across all trees.

pub fn oob_score(&self) -> Option<f64>

Out-of-bag accuracy score (available after fit with bootstrap=true).

pub fn n_trees(&self) -> usize

Number of trained trees.

pub fn trees(&self) -> &[DecisionTreeClassifier]

Get individual trees (for visualization or inspection).

pub fn n_classes(&self) -> usize

Number of classes the model was trained on.

pub fn n_features(&self) -> usize

Number of features the model was trained on.

Trait Implementations

impl CalibrableClassifier for RandomForestClassifier

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict class labels.

fn predict_proba(&self, features: &[Vec<f64>]) -> Result<Vec<Vec<f64>>>

Predict class probabilities. Returns [n_samples][n_classes].

fn clone_box(&self) -> Box<dyn CalibrableClassifier>

Clone into a boxed trait object.

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 Default for RandomForestClassifier

fn default() -> Self

Returns the “default value” for a type.

impl PipelineModel for RandomForestClassifier

fn fit(&mut self, data: &Dataset) -> Result<()>

Train the model on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict on row-major feature matrix.

impl Tunable for RandomForestClassifier

fn set_param(&mut self, name: &str, _value: ParamValue) -> Result<()>

Apply a named hyperparameter.

fn clone_box(&self) -> Box<dyn Tunable>

Clone this model into a boxed trait object.

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict on row-major features.