Struct rusty_ai::forests::regressor::RandomForestRegressor

pub struct RandomForestRegressor<T: RealNumber> { /* private fields */ }

Implementations

impl<T: RealNumber> RandomForestRegressor<T>

pub fn new() -> Self

Creates a new RandomForestRegressor with default parameters.

Returns

A new instance of the RandomForestRegressor.

pub fn with_params( num_trees: Option<usize>, min_samples_split: Option<u16>, max_depth: Option<u16>, sample_size: Option<usize> ) -> Result<Self, Box<dyn Error>>

Creates a new RandomForestRegressor with the specified parameters.

Arguments

• num_trees - The number of trees in the random forest. If not specified, the default value is 3.
• min_samples_split - The minimum number of samples required to split an internal node. If not specified, the default value is 2.
• max_depth - The maximum depth of the decision trees. If not specified, there is no maximum depth.
• sample_size - The size of the random subsets of the training data used to train each tree. If not specified, the default value is calculated as the total number of samples divided by the number of trees.

Returns

A Result containing the RandomForestRegressor if the parameters are valid, or a Box<dyn Error> if an error occurs.

pub fn set_trees(&mut self, trees: Vec<DecisionTreeRegressor<T>>)

Sets the decision trees for the random forest regressor.

Arguments

• trees - A vector of DecisionTreeRegressor instances.

pub fn set_num_trees(&mut self, num_trees: usize) -> Result<(), Box<dyn Error>>

Sets the number of trees in the random forest regressor.

Arguments

• num_trees - The number of trees.

Returns

Returns Ok(()) if successful, otherwise returns an error.

pub fn set_sample_size( &mut self, sample_size: Option<usize> ) -> Result<(), Box<dyn Error>>

Sets the sample size for each tree in the random forest regressor.

Arguments

• sample_size - The sample size for each tree. Use None for full sample size.

Returns

Returns Ok(()) if successful, otherwise returns an error.

pub fn set_min_samples_split( &mut self, min_samples_split: u16 ) -> Result<(), Box<dyn Error>>

Sets the minimum number of samples required to split an internal node in each decision tree.

Arguments

• min_samples_split - The minimum number of samples required to split an internal node.

Returns

Returns Ok(()) if successful, otherwise returns an error.

pub fn set_max_depth( &mut self, max_depth: Option<u16> ) -> Result<(), Box<dyn Error>>

Sets the maximum depth of each decision tree in the random forest regressor.

Arguments

• max_depth - The maximum depth of each decision tree. Use None for unlimited depth.

Returns

Returns Ok(()) if successful, otherwise returns an error.

pub fn trees(&self) -> &Vec<DecisionTreeRegressor<T>>

Returns a reference to the decision trees in the random forest regressor.

pub fn num_trees(&self) -> usize

Returns the number of trees in the random forest regressor.

pub fn sample_size(&self) -> Option<usize>

Returns the sample size for each tree in the random forest regressor.

pub fn min_samples_split(&self) -> u16

Returns the minimum number of samples required to split an internal node in each decision tree.

pub fn max_depth(&self) -> Option<u16>

Returns the maximum depth of each decision tree in the random forest regressor.

pub fn fit( &mut self, dataset: &Dataset<T, T>, seed: Option<u64> ) -> Result<String, Box<dyn Error>>

Fits the random forest regressor to the given dataset.

Arguments

• dataset - The dataset to fit the random forest regressor to.
• seed - The seed for the random number generator. Use None for a random seed.

Returns

Returns a string indicating the completion of the fitting process if successful, otherwise returns an error.

pub fn predict( &self, features: &DMatrix<T> ) -> Result<DVector<T>, Box<dyn Error>>

Predicts the target values for the given features using the random forest regressor.

Arguments

• features - The features to predict the target values for.

Returns

Returns a vector of predicted target values if successful, otherwise returns an error.

Trait Implementations

impl<T: Clone + RealNumber> Clone for RandomForestRegressor<T>

fn clone(&self) -> RandomForestRegressor<T>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<T: Debug + RealNumber> Debug for RandomForestRegressor<T>

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

Formats the value using the given formatter.

impl<T: RealNumber> Default for RandomForestRegressor<T>

fn default() -> Self

Creates a new RandomForestRegressor with default parameters.

Returns

A new instance of the RandomForestRegressor.

impl<T: RealNumber> RegressionMetrics<T> for RandomForestRegressor<T>

fn mse( &self, y_true: &DVector<T>, y_pred: &DVector<T> ) -> Result<T, Box<dyn Error>>

Computes the mean squared error (MSE) between the true values and the predicted values.

fn mae( &self, y_true: &DVector<T>, y_pred: &DVector<T> ) -> Result<T, Box<dyn Error>>

Computes the mean absolute error (MAE) between the true values and the predicted values.

fn r2( &self, y_true: &DVector<T>, y_pred: &DVector<T> ) -> Result<T, Box<dyn Error>>

Computes the coefficient of determination (R^2) between the true values and the predicted values.