`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.

The library implements the classifier_protocol defined in the classifier_protocols library. It provides predicates for learning an ensemble classifier from a dataset, using it to make predictions (with class probabilities), 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#random_forest link in a web browser.

Loading

To load all entities in this library, load the loader.lgt file:

| ?- logtalk_load(random_forest(loader)).

Testing

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

| ?- logtalk_load(random_forest(tester)).

Features

Ensemble Learning: Combines multiple C4.5 decision trees for robust predictions
Bootstrap Sampling: Each tree is trained on a random sample with replacement
Feature Randomization: Random subset of features selected for each tree (default: sqrt(total_features))
Majority Voting: Final predictions determined by voting across all trees
Probability Estimation: Provides confidence scores based on vote proportions
Configurable Options: Number of trees and max features per tree via predicate options
Classifier Export: Learned classifiers can be exported as predicate clauses

Options

The following options can be passed to the learn/3 predicate:

number_of_trees(N): Number of trees in the forest (default: 10)
maximum_features_per_tree(N): Maximum number of features to consider per tree (default: sqrt(total_features))

Classifier Representation

The learned classifier is represented as a compound term with the functor chosen by the user when exporting the classifier and arity 2. The default functor is rf_classifier/3:

rf_classifier(Trees, ClassValues)

Where:

Trees: List of tree(C45Tree, AttributeNames) pairs
ClassValues: List of possible class values
Options: List of options used during learning

References

Breiman, L. (2001). “Random Forests”. Machine Learning, 45(1), 5-32.
Ho, T.K. (1995). “Random Decision Forests”. Proceedings of the 3rd International Conference on Document Analysis and Recognition.
Quinlan, J.R. (1993). “C4.5: Programs for Machine Learning”. Morgan Kaufmann.

Usage

Learning a Classifier

% Learn a random forest with default options (10 trees)
| ?- random_forest::learn(play_tennis, Classifier).
...

% Learn with custom options
| ?- random_forest::learn(play_tennis, Classifier, [number_of_trees(20), maximum_features_per_tree(2)]).
...

Making Predictions

% Predict class for a new instance
| ?- random_forest::learn(play_tennis, Classifier),
     random_forest::predict(Classifier, [outlook-sunny, temperature-hot, humidity-high, wind-weak], Class).
Class = no
...

% Get probability distribution from ensemble voting
| ?- random_forest::learn(play_tennis, Classifier),
     random_forest::predict_probabilities(Classifier, [outlook-overcast, temperature-mild, humidity-normal, wind-weak], Probabilities).
Probabilities = [yes-0.9, no-0.1]
...

Exporting the Classifier

% Export as predicate clauses
| ?- random_forest::learn(play_tennis, Classifier),
     random_forest::classifier_to_clauses(play_tennis, Classifier, my_forest, Clauses).
Clauses = [my_forest(random_forest_classifier(...))]
...

% Export to a file
| ?- random_forest::learn(play_tennis, Classifier),
     random_forest::classifier_to_file(play_tennis, Classifier, my_forest, 'forest.pl').
...

Using a Saved Classifier

% Load and use a previously saved classifier
| ?- logtalk_load('forest.pl'),
     my_forest(Classifier),
     random_forest::predict(Classifier, [outlook-sunny, temperature-cool, humidity-normal, wind-weak], Class).
Class = yes
...

Printing the Classifier

% Print a summary of the random forest
| ?- random_forest::learn(play_tennis, Classifier),
     random_forest::print_classifier(Classifier).

Random Forest Classifier
========================

Number of trees: 10
Class values: [yes,no]
Options: [number_of_trees(10)]

Trees:
  Tree 1 (features: [outlook,humidity]):
    -> tree rooted at outlook
  ...
...

random_forest