Struct DecisionTreeRegressor 

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

CART decision tree for regression.

Implementations

impl DecisionTreeRegressor

pub fn new() -> Self

Create a new regressor with default parameters.

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

Set maximum tree depth.

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

Set minimum samples required to split.

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

Set minimum samples required in a leaf.

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

Set maximum features per split (for random forest).

pub fn ccp_alpha(self, alpha: f64) -> Self

Set cost-complexity pruning parameter.

Subtrees with effective alpha ≤ ccp_alpha are pruned after tree construction. A value of 0.0 (default) disables pruning. Larger values produce smaller, more regularized trees.

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

Train on a dataset.

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

Predict values.

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

Feature importances.

pub fn flat_tree(&self) -> Option<&FlatTree>

Get the flat tree.

pub fn n_features(&self) -> usize

Number of features.

Trait Implementations

impl Clone for DecisionTreeRegressor

fn clone(&self) -> DecisionTreeRegressor

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Default for DecisionTreeRegressor

fn default() -> Self

Returns the “default value” for a type.

impl PipelineModel for DecisionTreeRegressor

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 DecisionTreeRegressor

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.