Struct LogisticRegression 

pub struct LogisticRegression { /* private fields */ }

Online logistic regression model.

Predicts P(y=1 | x) = sigmoid(w·x + b). Learning uses the gradient of the binary log loss w.r.t. the logit, which simplifies to p - y.

Implementations

impl LogisticRegression

pub fn new( feature_count: usize, config: LogisticRegressionConfig, ) -> Result<Self, RillError>

Create a new logistic regression model.

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

The learned weights.

pub const fn intercept(&self) -> f64

The learned intercept (bias).

Trait Implementations

impl Clone for LogisticRegression

fn clone(&self) -> LogisticRegression

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 OnlineBinaryClassifier for LogisticRegression

fn feature_count(&self) -> usize

The number of features the model expects.

fn samples_seen(&self) -> u64

How many training samples the model has seen so far.

fn predict_proba(&self, features: &[f64]) -> Result<f64, RillError>

Predict the probability of the positive class.

fn learn(&mut self, features: &[f64], target: bool) -> Result<(), RillError>

Update the model using a single labeled sample.

fn reset(&mut self)

Reset the model to its initial state.

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

Predict the boolean class label using a 0.5 threshold.