`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 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

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

naive_bayes