Struct DecisionTreeClassifier 

pub struct DecisionTreeClassifier<T> { /* private fields */ }

Decision tree classifier (simplified stub for AdaBoost)

Implementations

impl DecisionTreeClassifier<Untrained>

pub fn new() -> Self

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

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

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

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

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

impl DecisionTreeClassifier<Trained>

pub fn predict(&self, x: &Array2<Float>) -> Result<Array1<Int>>

Trait Implementations

impl<T: Clone> Clone for DecisionTreeClassifier<T>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<T: Debug> Debug for DecisionTreeClassifier<T>

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

Formats the value using the given formatter.

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>>> for DecisionTreeClassifier<Untrained>

type Fitted = DecisionTreeClassifier<Trained>

The fitted model type

fn fit(self, _x: &Array2<Float>, _y: &Array1<Int>) -> 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