Struct Ridge 

#[non_exhaustive]
pub struct Ridge { /* private fields */ }

Ridge regression — LinearRegression with L2 regularization.

This is a thin wrapper around LinearRegression that provides a more discoverable API for users coming from scikit-learn’s Ridge class.

Example

use scry_learn::linear::Ridge;

let data = Dataset::new(
    vec![vec![1.0, 2.0, 3.0, 4.0, 5.0]],
    vec![2.0, 4.0, 6.0, 8.0, 10.0],
    vec!["x".into()],
    "y",
);

let mut model = Ridge::new(1.0);
model.fit(&data).unwrap();

Implementations

impl Ridge

pub fn new(alpha: f64) -> Self

Create a new Ridge regression model with the given L2 regularization strength.

Equivalent to LinearRegression::new().alpha(alpha).

pub fn fit(&mut self, data: &Dataset) -> Result<()>

Train the model on the given dataset.

pub fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict target values for the given feature matrix.

pub fn coefficients(&self) -> &[f64]

Get the learned coefficients.

pub fn intercept(&self) -> f64

Get the learned intercept.

Trait Implementations

impl Clone for Ridge

fn clone(&self) -> Ridge

Returns a duplicate of the value.

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

Performs copy-assignment from source.