Struct DecisionTreeRegressor 

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

Decision Tree Regressor

Implementations

impl<T: RealNumber> DecisionTreeRegressor<T>

pub fn new() -> Self

Creates a new instance of the decision tree regressor with default parameters.

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

Creates a new instance of the decision tree regressor with custom parameters.

Arguments

• min_samples_split - The minimum number of samples required to split an internal node.
• max_depth - The maximum depth of the tree.

Returns

A new instance of the decision tree regressor with the specified parameters.

Errors

This method will return an error if the minimum number of samples to split is less than 2 or if the maximum depth is less than 1.

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.

Arguments

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

Errors

This method will return an error if the minimum number of samples to split is less than 2.

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

Sets the maximum depth of the tree.

Arguments

• max_depth - The maximum depth of the tree.

Errors

This method will return an error if the maximum depth is less than 1.

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

Returns the maximum depth of the tree.

pub fn min_samples_split(&self) -> u16

Returns the minimum number of samples required to split an internal node.

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

Builds the decision tree from a dataset.

Arguments

• dataset - The dataset containing features and labels.

Returns

A string indicating that the tree was built successfully.

Errors

This method will return an error if the tree couldn’t be built.

pub fn predict( &self, prediction_features: &DMatrix<T>, ) -> Result<DVector<T>, String>

Predicts the labels for new data.

Arguments

• features - The matrix of features for the new data.

Returns

A vector containing the predicted target values for the new data.

Errors

This method will return an error if the tree wasn’t built yet.

Trait Implementations

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

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

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

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

Formats the value using the given formatter.

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

fn default() -> Self

Creates a new instance of the decision tree regressor with default parameters.

impl<T: RealNumber> RegressionMetrics<T> for DecisionTreeRegressor<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.