.. _library_naive_bayes:

naive_bayes

Naive Bayes classifier supporting both categorical and continuous (Gaussian) features with Laplace smoothing.

The library implements the classifier_protocol defined in the classifier_protocols library. It provides predicates for learning a classifier from a dataset, using it to make predications, and exporting it as a list of predicate clauses or to a file.

Datasets are represented as objects implementing the dataset_protocol protocol from the classifier_protocols library. See test_files directory for examples.

API documentation

Open the `../../docs/library_index.html#naive_bayes <../../docs/library_index.html#naive_bayes>`__ link in a web browser.

Loading

To load this library, load the loader.lgt file:

::

| ?- logtalk_load(naive_bayes(loader)).

Testing

To test this library predicates, load the tester.lgt file:

::

| ?- logtalk_load(naive_bayes(tester)).

Features

• Categorical Features: Handles discrete-valued features with Laplace smoothing
• Continuous Features: Uses Gaussian (normal) distribution for numeric features
• Classifier Export: Learned classifiers can be exported as predicate clauses
• Probability Estimation: Provides both class predictions and probability distributions

Usage

Learning a Classifier ~~~~~~~~~~~~~~~~~~~~~

::

% Learn from a dataset object | ?- naive_bayes::learn(my_dataset, Classifier). ...

Making Predictions ~~~~~~~~~~~~~~~~~~

::

% Predict class for a new instance and the probability distribution | ?- Instance = [...], naive_bayes::learn(my_dataset, Classifier), naive_bayes::predict(Classifier, Instance, PredictedClass), naive_bayes::predict_probability(Classifier, Instance, Probabilities). PredictedClass = ..., Probabilities = [...] ...

Exporting the Classifier ~~~~~~~~~~~~~~~~~~~~~~~~

Learned classifiers can be exported as a list of clauses or to a file for later use.

::

% Export as predicate clauses | ?- naive_bayes::learn(my_dataset, Classifier), naive_bayes::classifier_to_clauses(Classifier, my_classifier, Clauses). Clauses = [my_classifier(...)] ...

% Export to a file | ?- naive_bayes::learn(my_dataset, Classifier), naive_bayes::classifier_to_file(Classifier, my_classifier, 'classifier.pl'). ...

Using a learned classifier ~~~~~~~~~~~~~~~~~~~~~~~~~~

Learned and saved classifiers can later be used for predictions without needing to access the original training dataset.

::

% Later, load the file and use the classifier | ?- consult('classifier.pl'), my_classifier(Classifier), Instance = [...], naive_bayes::predict(Classifier, Instance, Class). Class = ... ...

Classifier Representation

The learned classifier is represented as a compound term with the functor chosen by the user when exporting the classifier and arity 5. The default functor is nb_classifier:

::

nb_classifier(Classes, ClassPriors, AttributeNames, FeatureTypes, FeatureParams)

Where:

• Classes: List of class labels
• ClassPriors: List of Class-Prior probability pairs
• AttributeNames: List of attribute names in order
• FeatureTypes: List of types (categorical or continuous)
• FeatureParams: List of learned parameters for each feature

References

1. Rish, I. (2001). "An empirical study of the naive Bayes classifier".
2. Russell, S. & Norvig, P. (2020). "Artificial Intelligence: A Modern Approach".
3. Mitchell, T. (1997). "Machine Learning". Chapter 6: Bayesian Learning.