Struct MultiTaskKernelRidgeRegression 

pub struct MultiTaskKernelRidgeRegression<State = Untrained> {
    pub approximation_method: ApproximationMethod,
    pub alpha: Float,
    pub task_regularization: TaskRegularization,
    pub solver: Solver,
    pub random_state: Option<u64>,
    /* private fields */
}

Multi-Task Kernel Ridge Regression

Performs kernel ridge regression simultaneously across multiple related tasks, with optional cross-task regularization to encourage similarity between tasks.

This is particularly useful when you have multiple regression targets that are related and can benefit from shared representations and joint learning.

Parameters

• approximation_method - Method for kernel approximation
• alpha - Within-task regularization parameter
• task_regularization - Cross-task regularization strategy
• solver - Method for solving the linear system
• random_state - Random seed for reproducibility

Examples

use sklears_kernel_approximation::kernel_ridge_regression::{

Fields

approximation_method: ApproximationMethod

alpha: Float

task_regularization: TaskRegularization

solver: Solver

random_state: Option<u64>

Implementations

impl MultiTaskKernelRidgeRegression<Untrained>

pub fn new(approximation_method: ApproximationMethod) -> Self

Create a new multi-task kernel ridge regression model

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

Set regularization parameter

pub fn task_regularization(self, regularization: TaskRegularization) -> Self

Set cross-task regularization strategy

pub fn solver(self, solver: Solver) -> Self

Set solver method

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

Set random state for reproducibility

impl MultiTaskKernelRidgeRegression<Trained>

pub fn n_tasks(&self) -> usize

Get the number of tasks

pub fn weights(&self) -> Option<&Array2<Float>>

Get the fitted weights for all tasks

pub fn task_weights(&self, task_idx: usize) -> Result<Array1<Float>>

Get the weights for a specific task

pub fn predict_task( &self, x: &Array2<Float>, task_idx: usize, ) -> Result<Array1<Float>>

Predict for a specific task only

Trait Implementations

impl<State: Clone> Clone for MultiTaskKernelRidgeRegression<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for MultiTaskKernelRidgeRegression<State>

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

Formats the value using the given formatter.

impl Estimator for MultiTaskKernelRidgeRegression<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<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for MultiTaskKernelRidgeRegression<Untrained>

type Fitted = MultiTaskKernelRidgeRegression<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &Array2<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; 2]>>> for MultiTaskKernelRidgeRegression<Trained>

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

Make predictions on the provided data

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

Make predictions with confidence intervals