naive_bayes_classifier — Logtalk 3.100.1

# `naive_bayes_classifier`

Naive Bayes probabilistic classifier based on Bayes theorem with strong (naive) independence assumptions between features and supporting both categorical and continuous (Gaussian) features with Laplace smoothing.

The library implements the `classifier_protocol` defined in the `classification_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 `classification_protocols` library. See `test_files` directory for examples.

## API documentation

Open the ../../docs/library_index.html#naive_bayes_classifier link in a web browser.

## Loading

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

    | ?- logtalk_load(naive_bayes_classifier(loader)).

## Testing

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

    | ?- logtalk_load(naive_bayes_classifier(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_classifier::learn(my_dataset, Classifier).
    ...

### Making Predictions

    % Predict class for a new instance and the probability distribution
    | ?- Instance = [...],
         naive_bayes_classifier::learn(my_dataset, Classifier),
         naive_bayes_classifier::predict(Classifier, Instance, PredictedClass),
         naive_bayes_classifier::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_classifier::learn(my_dataset, Classifier),
         naive_bayes_classifier::export_to_clauses(Classifier, my_classifier, Clauses).
    Clauses = [my_classifier(...)]
    ...

    % Export to a file
    | ?- naive_bayes_classifier::learn(my_dataset, Classifier),
         naive_bayes_classifier::export_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_classifier::predict(Classifier, Instance, Class).
    Class = ...
    ...

## Classifier representation

The learned classifier is represented as a compound term:

    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

When exported using `export_to_clauses/4` or `export_to_file/4`, this classifier term is serialized directly as the single argument of the generated predicate clause so that the exported model can be loaded and reused as-is.

## 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.