Naive Bayes#

Categorical Naive Bayes is a probability-based classifier that uses counting and Bayes' Theorem to derive the probabilities of a class given a sample of categorical features. The term naive refers to the fact that Naive Bayes treats each feature as if it was independent of the others even though this is usually not the case in real life.

Note: Each partial train has the overhead of recomputing the probability mass function for each feature per class. As such, it is better to train with fewer but larger training sets.

Interfaces: Estimator, Learner, Online, Probabilistic, Persistable

Data Type Compatibility: Categorical


# Param Default Type Description
1 alpha 1.0 float The amount of additive (Laplace/Lidstone) smoothing applied to the probabilities.
2 priors null array The class prior probabilities as an associative array with class labels as keys and the prior probabilities as values. If null, then the learner will compute these values from the training set.

Additional Methods#

Return the class prior probabilities:

public priors() : ?array

Return the counts for each category per class:

public counts() : array


use Rubix\ML\Classifiers\NaiveBayes;

$estimator = new NaiveBayes(2.5, [
    'spam' => 0.3,
    'not spam' => 0.7,