rbbr_predictor: Predict Using a Trained RBBR Model

In RBBR: Regression-Based Boolean Rule Inference

Predict Using a Trained RBBR Model

Description

Make predictions for new datapoints by utilizing a trained RBBR model.

Usage

rbbr_predictor(
  trained_model,
  data_test,
  num_top_rules = 1,
  slope = 10,
  num_cores = 1,
  verbose = FALSE
)

Arguments

trained_model	Model returned by 'rbbr_train()'
data_test	The new dataset for which we want to predict the target class or label probability. Each sample is represented as a row, and features are in columns.
num_top_rules	Number of Boolean rules with the best Bayesian Information Criterion (BIC) scores to be used for prediction. The default value is 1.
slope	The slope parameter for the sigmoid activation function. Default is 10.
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 are shown. Default is FALSE.

Value

Numeric vector of predicted probabilities (length = nrow(data_test))

Examples

# Load dataset
data(example_data)

# Inspect loaded data
head(XOR_data)

# For fast run, use the first three input features to predict target class in column 11
data_train <- XOR_data[1:800, c(1,2,3,11)]
data_test <- XOR_data[801:1000, c(1,2,3,11)]

# training model
trained_model <- rbbr_train(data_train,
                            max_feature = 2,
                            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 = 1,
                                   num_cores = 1, verbose = TRUE)

head(predicted_label_probabilities)
head(labels) # true labels

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