Struct LogisticRegression 

pub struct LogisticRegression { /* private fields */ }

L2-penalised logistic regression with an unpenalised intercept, fitted by Newton’s method with backtracking line search.

Minimises C · Σ logloss + ½‖w‖², the same objective as scikit-learn’s LogisticRegression(C=c); the default C = 1 matches scikit-learn’s default.

Implementations

impl LogisticRegression

pub fn new(c: f64) -> Self

Inverse regularisation strength c (larger = weaker penalty).

pub fn unpenalized() -> Self

No penalty. Fails to converge if the classes are perfectly separable.

pub fn tol(self, tol: f64) -> Self

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

pub fn coef(&self) -> Option<&Array1<f64>>

pub fn intercept(&self) -> f64

Trait Implementations

impl Classifier for LogisticRegression

fn fit(&mut self, x: ArrayView2<'_, f64>, y: ArrayView1<'_, f64>) -> Result<()>

fn fit_weighted( &mut self, x: ArrayView2<'_, f64>, y: ArrayView1<'_, f64>, w: ArrayView1<'_, f64>, ) -> Result<()>

Fit minimising a w-weighted loss (needed by Lplr’s instrument score). The default reports that weights are unsupported.

fn predict_proba(&self, x: ArrayView2<'_, f64>) -> Result<Array1<f64>>

fn fresh(&self) -> Box<dyn Classifier>

impl Clone for LogisticRegression

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LogisticRegression

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

Formats the value using the given formatter.

impl Default for LogisticRegression

fn default() -> Self

Returns the “default value” for a type.