Struct MultiTargetDecisionTreeClassifier 

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

Multi-Target Decision Tree Classifier

A decision tree classifier that can handle multiple target variables simultaneously. Uses joint entropy/gini reduction for optimal splits across all targets.

Examples

use sklears_multioutput::MultiTargetDecisionTreeClassifier;
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![[0, 1], [1, 0], [1, 1], [0, 0]]; // Two binary classification targets

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

Implementations

impl MultiTargetDecisionTreeClassifier<Untrained>

pub fn new() -> Self

Create a new MultiTargetDecisionTreeClassifier 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 criterion(self, criterion: ClassificationCriterion) -> Self

Set the split criterion

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

Set the random state for reproducible results

impl MultiTargetDecisionTreeClassifier<MultiTargetDecisionTreeClassifierTrained>

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

Get feature importances

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

Predict class probabilities for each target

Trait Implementations

impl<S: Clone> Clone for MultiTargetDecisionTreeClassifier<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MultiTargetDecisionTreeClassifier<S>

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

Formats the value using the given formatter.

impl Default for MultiTargetDecisionTreeClassifier<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MultiTargetDecisionTreeClassifier<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<i32>, Dim<[usize; 2]>>> for MultiTargetDecisionTreeClassifier<Untrained>

type Fitted = MultiTargetDecisionTreeClassifier<MultiTargetDecisionTreeClassifierTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &Array2<i32>, ) -> 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

impl Predict<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 2]>>> for MultiTargetDecisionTreeClassifier<MultiTargetDecisionTreeClassifierTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<i32>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals