Struct MultinomialNB 

#[non_exhaustive]
pub struct MultinomialNB { /* private fields */ }

Multinomial Naive Bayes — for count/frequency features.

Models each class as a multinomial distribution over features. Well-suited for document classification with term frequencies.

Uses Laplace smoothing (additive smoothing) to handle zero counts.

Implementations

impl MultinomialNB

pub fn new() -> Self

Create a new Multinomial Naive Bayes classifier.

pub fn alpha(self, a: f64) -> Self

Set Laplace smoothing parameter (default 1.0).

pub fn class_weight(self, cw: ClassWeight) -> Self

Set class weighting strategy for imbalanced datasets.

pub fn fit(&mut self, data: &Dataset) -> Result<()>

Train the model on a dataset.

Features should be non-negative counts or frequencies.

pub fn fit_sparse(&mut self, features: &CscMatrix, target: &[f64]) -> Result<()>

Fit on sparse features (CSC format) — perfect for TF-IDF.

Sums non-zero entries per class per feature for count-based likelihood.

pub fn predict_sparse(&self, features: &CsrMatrix) -> Result<Vec<f64>>

Predict from sparse features (CSR format).

pub fn predict_proba_sparse( &self, features: &CsrMatrix, ) -> Result<Vec<Vec<f64>>>

Predict probabilities from sparse features (CSR format).

Only accumulates log-probability for non-zero features (zero × log_prob = 0).

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

Predict class labels.

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

Predict normalized probabilities for each class.

Trait Implementations

impl CalibrableClassifier for MultinomialNB

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

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

Predict class labels.

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

Predict class probabilities. Returns [n_samples][n_classes].

fn clone_box(&self) -> Box<dyn CalibrableClassifier>

Clone into a boxed trait object.

impl Clone for MultinomialNB

fn clone(&self) -> MultinomialNB

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Default for MultinomialNB

fn default() -> Self

Returns the “default value” for a type.

impl PipelineModel for MultinomialNB

fn fit(&mut self, data: &Dataset) -> Result<()>

Train the model on a dataset.

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

Predict on row-major feature matrix.

impl Tunable for MultinomialNB

fn set_param(&mut self, name: &str, _value: ParamValue) -> Result<()>

Apply a named hyperparameter.

fn clone_box(&self) -> Box<dyn Tunable>

Clone this model into a boxed trait object.

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

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

Predict on row-major features.