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Gaussian Naive Bayes

Gaussian Naive Bayes is a version of the Naive Bayes classifier for continuous features. It places a probability density function (PDF) over the features conditioned on a class basis and uses Bayes' Theorem to derive the final probabilities. In addition to the naive feature independence assumption, Gaussian Naive Bayes also assumes that all features are normally (Gaussian) distributed.

Interfaces: Estimator, Learner, Online, Probabilistic, Persistable

Data Type Compatibility: Continuous

Parameters

#	Name	Default	Type	Description
1	priors	null	array	The class prior probabilities as an associative array with class labels as keys and their prior probabilities as values totalling 1. If null, then priors will automatically be computed from the training data.
2	smoothing	1e-9	float	The amount of epsilon smoothing added to the variance of each feature.

Example

use Rubix\ML\Classifiers\GaussianNB;

$estimator = new GaussianNB([
    'benign' => 0.9,
    'malignant' => 0.1,
], 1e-9);

Additional Methods

Return the class prior probabilities:

public priors() : float[]|null

Return the mean of each feature column for each class:

public means() : array[]|null

Return the variance of each feature column for each class:

public variances() : array[]|null

References

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1. T. F. Chan et al. (1979). Updating Formulae and a Pairwise Algorithm for Computing Sample Variances. ↩