Struct ModelTree 

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

Model Tree for regression with linear models in leaves

Implementations

impl ModelTree<Untrained>

pub fn new() -> Self

Create a new Model Tree

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

Set the maximum tree depth

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

Set the minimum samples required to split

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

Set the minimum samples required at a leaf

pub fn min_std_dev_reduction(self, min_std_dev_reduction: Float) -> Self

Set the minimum standard deviation reduction

pub fn prune(self, prune: bool) -> Self

Enable or disable pruning

pub fn leaf_model(self, leaf_model: LeafModelType) -> Self

Set the leaf model type

impl ModelTree<Trained>

pub fn n_features(&self) -> usize

Get the number of features

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

Get feature importances

pub fn tree(&self) -> &ModelTreeNode

Get the tree structure (root node)

Trait Implementations

impl Default for ModelTree<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for ModelTree<Untrained>

type Config = ModelTreeConfig

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 ModelTree<Untrained>

type Fitted = ModelTree<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &Array1<Float>) -> 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 ModelTree<Trained>

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

Make predictions on the provided data

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

Make predictions with confidence intervals