Struct RandomForestClassifier 

pub struct RandomForestClassifier { /* private fields */ }

Random Forest classifier - an ensemble of decision trees.

Combines multiple decision trees trained on bootstrap samples with random feature selection to reduce overfitting and improve accuracy.

Implementations

impl RandomForestClassifier

pub fn new(n_estimators: usize) -> RandomForestClassifier

Creates a new Random Forest classifier.

pub fn with_max_depth(self, max_depth: usize) -> RandomForestClassifier

Sets the maximum depth for each tree.

pub fn with_random_state(self, random_state: u64) -> RandomForestClassifier

Sets the random state for reproducibility.

pub fn fit(&mut self, x: &Matrix<f32>, y: &[usize]) -> Result<(), AprenderError>

Fits the random forest to training data.

Errors

Returns an error if fitting fails.

pub fn predict(&self, x: &Matrix<f32>) -> Vec<usize>

Makes predictions for input data.

pub fn score(&self, x: &Matrix<f32>, y: &[usize]) -> f32

Calculates accuracy score on test data.

pub fn predict_proba(&self, x: &Matrix<f32>) -> Matrix<f32>

Predict class probabilities for input features.

pub fn oob_prediction(&self) -> Option<Vec<usize>>

Returns Out-of-Bag (OOB) predictions for training samples.

pub fn oob_score(&self) -> Option<f32>

Returns Out-of-Bag (OOB) accuracy score.

pub fn feature_importances(&self) -> Option<Vec<f32>>

Returns feature importances based on Mean Decrease in Impurity (MDI).

For each split node, the contribution is the weighted gini decrease n_node·gini − n_left·left_gini − n_right·right_gini (sklearn tree/_tree.pyx::compute_feature_importances), averaged across trees and normalized to sum 1. See contracts/tree-feature-importances-mdi-v1.yaml.

pub fn save_safetensors<P>(&self, path: P) -> Result<(), String>

where P: AsRef<Path>,

Saves the Random Forest model to a SafeTensors file.

Errors

Returns an error if the model is unfitted or if saving fails.

pub fn load_safetensors<P>(path: P) -> Result<RandomForestClassifier, String>

where P: AsRef<Path>,

Loads a Random Forest model from a SafeTensors file.

Errors

Returns an error if loading fails or if the file format is invalid.

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<(), Error>

Formats the value using the given formatter.

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

fn deserialize<__D>( __deserializer: __D, ) -> Result<RandomForestClassifier, <__D as Deserializer<'de>>::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Estimator for RandomForestClassifier

fn fit(&mut self, x: &Matrix<f32>, y: &Vector<f32>) -> Result<(), AprenderError>

Fits the model to training data.

fn predict(&self, x: &Matrix<f32>) -> Vector<f32>

Predicts target values for input data.

fn score(&self, x: &Matrix<f32>, y: &Vector<f32>) -> f32

Computes the score (R² for regression, accuracy for classification).

impl Serialize for RandomForestClassifier

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

where __S: Serializer,

Serialize this value into the given Serde serializer.