Struct DecisionTree 

pub struct DecisionTree<State = Untrained> { /* private fields */ }

Main Decision Tree structure that can be used for both classification and regression

Implementations

impl<State> DecisionTree<State>

pub fn new() -> Self

Create a new DecisionTree with default configuration

pub fn with_config(config: DecisionTreeConfig) -> Self

Create a new DecisionTree with custom configuration

pub fn builder() -> DecisionTreeBuilder<State>

Create a builder for configuring the decision tree

pub fn config(&self) -> &DecisionTreeConfig

Get the configuration of the decision tree

pub fn root(&self) -> Option<&TreeNode>

Get the root node of the tree (if fitted)

pub fn feature_importances(&self) -> Option<&Array1<f64>>

Get feature importances (if available)

pub fn n_features(&self) -> usize

Get the number of features the tree was trained on

pub fn n_samples(&self) -> usize

Get the number of training samples

pub fn depth(&self) -> usize

Get the depth of the tree (returns root node depth if available)

pub fn criterion(self, criterion: SplitCriterion) -> Self

Set the split criterion (fluent API)

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

Set the maximum depth of the tree (fluent API)

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

Set the minimum samples required to split an internal node (fluent API)

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

Set the minimum samples required to be at a leaf node (fluent API)

pub fn missing_values(self, strategy: MissingValueStrategy) -> Self

Set the missing value strategy (fluent API)

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

Set the random seed (fluent API)

impl DecisionTree<Untrained>

pub fn is_fitted(&self) -> bool

Check if the tree has been fitted (always false for Untrained trees)

pub fn n_classes(&self) -> usize

Get the number of classes (not available for untrained trees)

impl DecisionTree<Trained>

pub fn is_fitted(&self) -> bool

Check if the tree has been fitted (always true for Trained trees)

pub fn n_classes(&self) -> usize

Get the number of classes (for classification trees)

Trait Implementations

impl<State: Clone> Clone for DecisionTree<State>

fn clone(&self) -> DecisionTree<State>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for DecisionTree<State>

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

Formats the value using the given formatter.

impl<State> Default for DecisionTree<State>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator<Trained> for DecisionTree<Trained>

type Config = DecisionTreeConfig

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 Estimator for DecisionTree<Untrained>

type Config = DecisionTreeConfig

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<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for DecisionTree<Untrained>

type Fitted = DecisionTree<Trained>

The fitted model type

fn fit(self, x: &Array2<f64>, y: &Array1<f64>) -> Result<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<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for DecisionTree<Trained>

fn predict(&self, x: &Array2<f64>) -> Result<Array1<f64>>

Make predictions on the provided data

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

Make predictions with confidence intervals