object

naive_bayes

Naive Bayes classifier with Laplace smoothing and Gaussian distribution support. Learns from a dataset object implementing the dataset_protocol protocol and returns a classifier term that can be used for prediction and exported as predicate clauses.

Availability:

logtalk_load(naive_bayes(loader))

Author: Paulo Moura

Version: 1:0:0

Date: 2026-02-20

Compilation flags:

static, context_switching_calls

Implements:

public classifier_protocol

Uses:

format

list

pairs

population

Remarks:

• Algorithm: Naive Bayes is a probabilistic classifier based on Bayes theorem with strong (naive) independence assumptions between features.

• Categorical features: Uses Laplace smoothing to handle unseen feature values.

• Continuous features: Uses Gaussian (normal) distribution to model numeric features.

• Classifier representation: The learned classifier is represented by default as nb_classifier(Classes, ClassPriors, AttributeNames, FeatureTypes, FeatureParams) where FeatureParams contains the learned probabilities or statistics for each feature.

Inherited public predicates:

 classifier_to_clauses/4  classifier_to_file/4  learn/2  predict/3  print_classifier/1

Public predicates

predict_probabilities/3

Predicts class probabilities for a new instance using the learned classifier. Returns a list of Class-Probability pairs. The instance is a list of Attribute-Value pairs.

Compilation flags:

static

Template:

predict_probabilities(Classifier,Instance,Probabilities)

Mode and number of proofs:

predict_probabilities(+compound,+list,-list) - one

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Protected predicates

(no local declarations; see entity ancestors if any)

Private predicates

(no local declarations; see entity ancestors if any)

Operators

(none)

See also

dataset_protocol, isolation_forest, c45, knn, nearest_centroid, random_forest, ada_boost