Struct Ridge 

pub struct Ridge<F> {
    pub alpha: F,
    pub fit_intercept: bool,
}

Ridge regression (L2-regularized least squares).

Adds an L2 penalty to the ordinary least squares objective, which shrinks coefficients toward zero and can help with multicollinearity.

Type Parameters

• F: The floating-point type (f32 or f64).

Fields

alpha: F

Regularization strength. Larger values specify stronger regularization.

fit_intercept: bool

Whether to fit an intercept (bias) term.

Implementations

impl<F: Float> Ridge<F>

pub fn new() -> Self

Create a new Ridge with default settings.

Defaults: alpha = 1.0, fit_intercept = true.

pub fn with_alpha(self, alpha: F) -> Self

Set the regularization strength.

pub fn with_fit_intercept(self, fit_intercept: bool) -> Self

Set whether to fit an intercept term.

Trait Implementations

impl<F: Clone> Clone for Ridge<F>

fn clone(&self) -> Ridge<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for Ridge<F>

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

Formats the value using the given formatter.

impl<F: Float> Default for Ridge<F>

fn default() -> Self

Returns the “default value” for a type.

impl<F: Float + Send + Sync + ScalarOperand + FromPrimitive + 'static> Fit<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>> for Ridge<F>

fn fit( &self, x: &Array2<F>, y: &Array1<F>, ) -> Result<FittedRidge<F>, FerroError>

Fit the Ridge regression model using Cholesky decomposition.

Solves (X^T X + alpha * I)^{-1} X^T y.

Errors

Returns FerroError::ShapeMismatch if the number of samples in x and y differ. Returns FerroError::InvalidParameter if alpha is negative.

type Fitted = FittedRidge<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F> PipelineEstimator<F> for Ridge<F>

where F: Float + FromPrimitive + ScalarOperand + Send + Sync + 'static,

fn fit_pipeline( &self, x: &Array2<F>, y: &Array1<F>, ) -> Result<Box<dyn FittedPipelineEstimator<F>>, FerroError>

Fit this estimator on the given data.