rbbr_train: Trains RBBR to learn Boolean rules

In RBBR: Regression-Based Boolean Rule Inference

Trains RBBR to learn Boolean rules

Description

Regression-Based Boolean Rule (RBBR) inference is performed on datasets where the input and target features are either binarized or continuous within the [0,1] range.

Usage

rbbr_train(
  data,
  max_feature = 3,
  mode = "1L",
  slope = 10,
  weight_threshold = 0,
  balancing = TRUE,
  num_cores = NA,
  verbose = FALSE
)

Arguments

data	The dataset with scaled features within the [0,1] interval. Each row represents a sample and each column represents a feature. The target variable must be in the last column.
max_feature	The maximum number of input features allowed in a Boolean rule. The default value is 3.
mode	Choose between "1L" for fitting 1-layered models or "2L" for fitting 2-layered models. The default value is "1L".
slope	The slope parameter used in the Sigmoid activation function. The default value is 10.
weight_threshold	Conjunctions with weights above this threshold in the fitted ridge regression models will be printed as active conjunctions in the output. The default value is 0.
balancing	Logical. This is for adjusting the distribution of target classes or categories within a dataset to ensure that each class is adequately represented. The default value is TRUE. Set it to FALSE, if you don't need to perform the data balancing.
num_cores	Number of parallel workers to use for computation. Adjust according to your system. Default is NA (automatic selection).
verbose	Logical. If TRUE, progress messages and a progress bar are shown. Default is FALSE.

Value

This function outputs the predicted Boolean rules with the best Bayesian Information Criterion (BIC).

Examples

# Load dataset
data(example_data)

# Example for training a two-layer model
head(OR_data)

# For fast run, use the first three input features to predict target class in column five
data_train   <- OR_data[1:800, c(1,2,3,5)]
data_test    <- OR_data[801:1000, c(1,2,3,5)]

# training model
trained_model <- rbbr_train(data_train,
                           max_feature = 2,
                           mode = "2L",
                           balancing = FALSE,
                           num_cores = 1, verbose = TRUE)

head(trained_model$boolean_rules)

# testing model
data_test_x  <- data_test[ ,1:(ncol(data_test)-1)]
labels       <- data_test[ ,ncol(data_test)]

predicted_label_probabilities <- rbbr_predictor(trained_model,
                                   data_test_x,
                                   num_top_rules = 10,
                                   num_cores = 1, verbose = TRUE)

head(predicted_label_probabilities)

RBBR documentation built on Feb. 17, 2026, 5:07 p.m.