object

random_forest

Random Forest classifier using C4.5 decision trees as base learners. Builds an ensemble of decision trees trained on bootstrap samples with random feature subsets and combines their predictions through majority voting.

Availability:
logtalk_load(random_forest(loader))
Author: Paulo Moura
Version: 1:0:0
Date: 2026-02-20
Compilation flags:
static, context_switching_calls
Implements:
public classifier_protocol
Imports:
public options
Uses:
c45
fast_random
format
list
pairs
type
Remarks:

  • Algorithm: Random Forest is an ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes predicted by individual trees.

  • Bootstrap sampling: Each tree is trained on a bootstrap sample (random sample with replacement) of the training data.

  • Feature randomization: At each tree, a random subset of features is selected. The default number of features is sqrt(total_features).

  • Classifier representation: The learned classifier is represented by default as a rf_classifier(Trees, ClassValues, Options) term.

Inherited public predicates:
 check_option/1  check_options/1  classifier_to_clauses/4  classifier_to_file/4  default_option/1  default_options/1  learn/2  option/2  option/3  predict/3  print_classifier/1  valid_option/1  valid_options/1

Public predicates

learn/3

Learns a classifier from the given dataset object using the specified options.

Compilation flags:
static
Template:
learn(Dataset,Classifier,Options)
Mode and number of proofs:
learn(+object_identifier,-compound,+list(compound)) - one

predict_probabilities/3

Predicts class probabilities for a new instance using the learned classifier. Returns a list of Class-Probability pairs sorted by descending probability. 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

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, c45, isolation_forest, knn, naive_bayes, nearest_centroid, ada_boost