Struct RidgeClassifier 

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

Ridge Classifier

Implementations

impl RidgeClassifier<Untrained>

pub fn new() -> Self

Create a new RidgeClassifier with default configuration

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

Set the regularization strength

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

Set whether to fit the intercept

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

Set whether to normalize the features

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

Set the solver

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

Set the tolerance

impl RidgeClassifier<Trained>

pub fn coef(&self) -> &Array2<Float>

Get the coefficients

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

Get the intercept

pub fn classes(&self) -> &Array1<Int>

Get the classes

pub fn n_features_in(&self) -> usize

Get the number of features seen during fit

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

Get decision function (raw scores)

Trait Implementations

impl Default for RidgeClassifier<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for RidgeClassifier<Untrained>

type Float = f64

The numeric type used by this estimator

type Config = RidgeClassifierConfig

Configuration type for the estimator

type Error = SklearsError

Error type for the 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 Estimator for RidgeClassifier<Trained>

type Float = f64

The numeric type used by this estimator

type Config = RidgeClassifierConfig

Configuration type for the estimator

type Error = SklearsError

Error type for the 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<i32>, Dim<[usize; 1]>>> for RidgeClassifier<Untrained>

type Fitted = RidgeClassifier<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &Array1<Int>) -> 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<i32>, Dim<[usize; 1]>>> for RidgeClassifier<Trained>

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

Make predictions on the provided data

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

Make predictions with confidence intervals

impl Score<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>>> for RidgeClassifier<Trained>

type Float = f64

The numeric type for score calculation

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

Calculate the score of the model on the provided data