Table of Contents

Mojo struct

BernoulliNB

@memory_only
struct BernoulliNB

Naive Bayes classifier for multivariate Bernoulli models.

Suited for discrete, binary/boolean features. Each feature is binarized against binarize (if it isn't already boolean) before being modelled with an independent Bernoulli distribution per class.

Aliases

  • MODEL_ID = 13

Fields

  • alpha (Float32): Additive (Laplace/Lidstone) smoothing parameter. Must be non-negative.
  • binarize (Float32): Threshold for binarizing features: values strictly greater than this become 1, others become 0.

Implemented traits

AnyType, CV, Copyable, Deinitable, Movable

Methods

__init__

fn def __init__(out self, alpha: Float32 = 0, binarize: Float32 = 0)

Args:

  • alpha (Float32)
  • binarize (Float32)
  • self (Self)

Returns:

Self

fn def __init__(out self, params: Dict[String, String])

Construct from a hyperparameter dictionary.

Args:

  • params (Dict[String, String])
  • self (Self)

Returns:

Self

Raises:

fit

fn def fit(mut self, X: Matrix, y: Matrix)

Fit Bernoulli Naive Bayes classifier.

Args:

  • self (Self)
  • X (Matrix): Training features of shape (n_samples, n_features).
  • y (Matrix): Training labels of shape (n_samples, 1), encoded as contiguous non-negative integers starting at 0.

Raises:

predict

fn def predict(self, X: Matrix) -> Matrix

Predict class for X.

Args:

  • self (Self)
  • X (Matrix)

Returns:

Matrix: The predicted classes.

Raises:

save

fn def save(self, path: String)

Save model data necessary for prediction to the specified path.

Args:

  • self (Self)
  • path (String)

Raises:

load

@staticmethod
fn def load(path: String) -> Self

Load a saved model from the specified path for prediction.

Args:

  • path (String)

Returns:

Self

Raises:

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