Struct RobustKernelRidgeRegression 

pub struct RobustKernelRidgeRegression<State = Untrained> {
    pub approximation_method: ApproximationMethod,
    pub alpha: Float,
    pub robust_loss: RobustLoss,
    pub solver: Solver,
    pub max_iter: usize,
    pub tolerance: Float,
    pub random_state: Option<u64>,
    /* private fields */
}

Robust kernel ridge regression

This implements robust variants of kernel ridge regression that are resistant to outliers and noise in the data. Multiple robust loss functions are supported.

Parameters

• approximation_method - Method for kernel approximation
• alpha - Regularization strength
• robust_loss - Robust loss function to use
• solver - Method for solving the optimization problem
• max_iter - Maximum number of iterations for robust optimization
• tolerance - Convergence tolerance
• random_state - Random seed for reproducibility

Examples

use sklears_kernel_approximation::kernel_ridge_regression::{

Fields

approximation_method: ApproximationMethod

alpha: Float

robust_loss: RobustLoss

solver: Solver

max_iter: usize

tolerance: Float

random_state: Option<u64>

Implementations

impl RobustKernelRidgeRegression<Untrained>

pub fn new(approximation_method: ApproximationMethod) -> Self

Create a new robust kernel ridge regression model

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

Set regularization parameter

pub fn robust_loss(self, robust_loss: RobustLoss) -> Self

Set robust loss function

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

Set solver method

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

Set maximum iterations for robust optimization

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

Set convergence tolerance

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

Set random state for reproducibility

impl RobustKernelRidgeRegression<Trained>

pub fn weights(&self) -> Option<&Array1<Float>>

Get the fitted weights

pub fn sample_weights(&self) -> Option<&Array1<Float>>

Get the sample weights from robust fitting

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

Compute robust residuals and their weights

pub fn outlier_scores(&self) -> Option<Array1<Float>>

Get outlier scores (lower weight means more likely to be outlier)

Trait Implementations

impl<State: Clone> Clone for RobustKernelRidgeRegression<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for RobustKernelRidgeRegression<State>

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

Formats the value using the given formatter.

impl Estimator for RobustKernelRidgeRegression<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; 1]>>> for RobustKernelRidgeRegression<Untrained>

type Fitted = RobustKernelRidgeRegression<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 RobustKernelRidgeRegression<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