Struct LogisticRegression 

pub struct LogisticRegression<F> {
    pub c: F,
    pub max_iter: usize,
    pub tol: F,
    pub fit_intercept: bool,
}

Logistic regression classifier.

Uses L-BFGS optimization to minimize the regularized logistic loss. Supports both binary and multiclass (multinomial) classification.

Type Parameters

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

Fields

c: F

Inverse regularization strength. Smaller values specify stronger regularization (matching scikit-learn’s convention).

max_iter: usize

Maximum number of L-BFGS iterations.

tol: F

Convergence tolerance for the optimizer.

fit_intercept: bool

Whether to fit an intercept (bias) term.

Implementations

impl<F: Float> LogisticRegression<F>

pub fn new() -> Self

Create a new LogisticRegression with default settings.

Defaults: C = 1.0, max_iter = 1000, tol = 1e-4, fit_intercept = true.

pub fn with_c(self, c: F) -> Self

Set the inverse regularization strength.

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

Set the maximum number of iterations.

pub fn with_tol(self, tol: F) -> Self

Set the convergence tolerance.

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

Set whether to fit an intercept term.

Trait Implementations

impl<F: Clone> Clone for LogisticRegression<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for LogisticRegression<F>

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

Formats the value using the given formatter.

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

fn default() -> Self

Returns the “default value” for a type.

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

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

Fit the logistic regression model using L-BFGS optimization.

Errors

Returns FerroError::ShapeMismatch if the number of samples in x and y differ. Returns FerroError::InvalidParameter if C is not positive. Returns FerroError::InsufficientSamples if there are fewer than 2 distinct classes.

type Fitted = FittedLogisticRegression<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F> PipelineEstimator<F> for LogisticRegression<F>

where F: Float + ToPrimitive + 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.