Struct LinearRegression 

pub struct LinearRegression<F> {
    pub fit_intercept: bool,
    /* private fields */
}

Ordinary least squares linear regression.

Solves the normal equations using QR decomposition for numerical stability. The fit_intercept option controls whether a bias (intercept) term is included.

Type Parameters

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

Fields

fit_intercept: bool

Whether to fit an intercept (bias) term.

Implementations

impl<F: Float> LinearRegression<F>

pub fn new() -> Self

Create a new LinearRegression with default settings.

Defaults: fit_intercept = true.

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

Set whether to fit an intercept term.

Trait Implementations

impl<F: Clone> Clone for LinearRegression<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for LinearRegression<F>

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

Formats the value using the given formatter.

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

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

Fit the linear regression model.

Uses the centering trick with Cholesky normal equations for speed. Falls back to QR decomposition via faer if the normal equations are ill-conditioned.

Errors

Returns FerroError::ShapeMismatch if the number of samples in x and y differ. Returns FerroError::InsufficientSamples if there are fewer samples than features. Returns FerroError::NumericalInstability if the system is singular.

type Fitted = FittedLinearRegression<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl PipelineEstimator<f64> for LinearRegression<f64>

fn fit_pipeline( &self, x: &Array2<f64>, y: &Array1<f64>, ) -> Result<Box<dyn FittedPipelineEstimator<f64>>, FerroError>

Fit this estimator on the given data.