Struct FittedLogisticRegression 

pub struct FittedLogisticRegression<F> { /* private fields */ }

Fitted logistic regression classifier.

Stores the learned coefficients, intercept, and class labels. For binary classification, stores a single coefficient vector. For multiclass, stores one coefficient vector per class.

Implementations

impl<F: Float + Send + Sync + ScalarOperand + 'static> FittedLogisticRegression<F>

pub fn weight_matrix(&self) -> &Array2<F>

Returns a reference to the full weight matrix.

For binary classification, shape is (1, n_features). For multiclass, shape is (n_classes, n_features).

pub fn intercept_vec(&self) -> &Array1<F>

Returns a reference to the intercept vector (one per class).

pub fn is_binary(&self) -> bool

Returns whether this is a binary classification model.

pub fn predict_proba(&self, x: &Array2<F>) -> Result<Array2<F>, FerroError>

Predict class probabilities for the given feature matrix.

For binary classification, returns an array of shape (n_samples, 2). For multiclass, returns shape (n_samples, n_classes).

Errors

Returns FerroError::ShapeMismatch if the number of features does not match the fitted model.

Trait Implementations

impl<F: Clone> Clone for FittedLogisticRegression<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for FittedLogisticRegression<F>

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

Formats the value using the given formatter.

impl<F: Float + Send + Sync + ScalarOperand + 'static> HasClasses for FittedLogisticRegression<F>

fn classes(&self) -> &[usize]

Returns the sorted list of unique class labels.

fn n_classes(&self) -> usize

Returns the number of distinct classes.

impl<F: Float + Send + Sync + ScalarOperand + 'static> HasCoefficients<F> for FittedLogisticRegression<F>

fn coefficients(&self) -> &Array1<F>

Returns a reference to the learned coefficient vector.

fn intercept(&self) -> F

Returns the learned intercept (bias) term.

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

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

Predict class labels for the given feature matrix.

Returns the class with the highest predicted probability.

Errors

Returns FerroError::ShapeMismatch if the number of features does not match the fitted model.

type Output = ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>>

The prediction output type (e.g., ndarray::Array1<F> or ndarray::Array1<usize>).

type Error = FerroError

The error type returned by predict.