Struct GradientBoostingMultiOutput 

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

Gradient Boosting Multi-Output

A gradient boosting implementation that can handle multiple output variables simultaneously. This implementation builds an additive model of weak learners (multi-target regression trees) in a forward stage-wise fashion, optimizing a loss function for all targets jointly.

Mathematical Foundation

For multi-target regression, gradient boosting minimizes:

• L(y, F(x)) = Σ_i Σ_j (y_ij - F_j(x_i))^2 for all samples i and targets j
• F_j(x) = F_0j + Σ_m ρ_m * h_mj(x) where h_mj are weak learners for target j at stage m
• At each stage, fit weak learners to negative gradients: -∂L/∂F_j

Examples

use sklears_multioutput::GradientBoostingMultiOutput;
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 gbm = GradientBoostingMultiOutput::new()
    .n_estimators(50)
    .learning_rate(0.1)
    .max_depth(3);

let fitted = gbm.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl GradientBoostingMultiOutput<Untrained>

pub fn new() -> Self

Create a new GradientBoostingMultiOutput instance

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

Set the number of estimators

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

Set the learning rate

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

Set the maximum depth

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

Set the minimum samples to split

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

Set the random state

impl GradientBoostingMultiOutput<GradientBoostingMultiOutputTrained>

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

Get the feature importance scores

pub fn training_loss_history(&self) -> Vec<Float>

Get the training loss history

Trait Implementations

impl<S: Clone> Clone for GradientBoostingMultiOutput<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for GradientBoostingMultiOutput<S>

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

Formats the value using the given formatter.

impl Default for GradientBoostingMultiOutput<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for GradientBoostingMultiOutput<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<ViewRepr<&f64>, Dim<[usize; 2]>>> for GradientBoostingMultiOutput<Untrained>

type Fitted = GradientBoostingMultiOutput<GradientBoostingMultiOutputTrained>

The fitted model type

fn fit( self, x: &ArrayView2<'_, Float>, y: &ArrayView2<'_, 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

impl Predict<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for GradientBoostingMultiOutput<GradientBoostingMultiOutputTrained>

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

Make predictions on the provided data

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

Make predictions with confidence intervals