Struct MultiTargetRegressionTree 

pub struct MultiTargetRegressionTree<S = Untrained> { /* private fields */ }

Multi-Target Regression Tree

A decision tree regressor that can handle multiple target variables simultaneously. Uses joint variance reduction for optimal splits across all targets.

Examples

use sklears_multioutput::MultiTargetRegressionTree;
use sklears_core::traits::{Predict, Fit};
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 1.0], [4.0, 4.0]];
let y = array![[1.5, 2.5], [2.5, 3.5], [3.5, 1.5], [4.5, 4.5]];

let tree = MultiTargetRegressionTree::new()
    .max_depth(Some(3))
    .min_samples_split(2);
let trained_tree = tree.fit(&X.view(), &y).unwrap();
let predictions = trained_tree.predict(&X.view()).unwrap();

Implementations

impl MultiTargetRegressionTree<Untrained>

pub fn new() -> Self

Create a new MultiTargetRegressionTree instance

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

Set the maximum depth of the tree

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

Set the minimum number of samples required to split an internal node

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

Set the minimum number of samples required to be at a leaf node

pub fn random_state(self, random_state: Option<u64>) -> Self

Set the random state for reproducible results

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

Get the maximum depth of the tree

pub fn get_min_samples_split(&self) -> usize

Get the minimum number of samples required to split an internal node

pub fn get_min_samples_leaf(&self) -> usize

Get the minimum number of samples required to be at a leaf node

pub fn get_random_state(&self) -> Option<u64>

Get the random state

impl MultiTargetRegressionTree<MultiTargetRegressionTreeTrained>

pub fn feature_importances(&self) -> &Array1<Float>

Get the feature importances

pub fn n_features(&self) -> usize

Get the number of features

pub fn n_targets(&self) -> usize

Get the number of targets

pub fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>>

Predict using the fitted tree

Trait Implementations

impl<S: Clone> Clone for MultiTargetRegressionTree<S>

fn clone(&self) -> MultiTargetRegressionTree<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MultiTargetRegressionTree<S>

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

Formats the value using the given formatter.

impl Default for MultiTargetRegressionTree<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MultiTargetRegressionTree<Untrained>

type Config = ()

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for MultiTargetRegressionTree<Untrained>

type Fitted = MultiTargetRegressionTree<MultiTargetRegressionTreeTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &Array2<Float>, ) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping