Bojarczuk_GP-C: Bojarczuk_GP_C KEEL Classification Algorithm

In RKEEL: Using 'KEEL' in R Code

Bojarczuk_GP_C KEEL Classification Algorithm

Description

Bojarczuk_GP_C Classification Algorithm from KEEL.

Usage

Bojarczuk_GP_C(train, test, population_size, max_generations,
   max_deriv_size, rec_prob, copy_prob, seed)

Arguments

train	Train dataset as a data.frame object
test	Test dataset as a data.frame object
population_size	population_size. Default value = 200
max_generations	max_generations. Default value = 200
max_deriv_size	max_deriv_size. Default value = 20
rec_prob	rec_prob. Default value = 0.8
copy_prob	copy_prob. Default value = 0.01
seed	Seed for random numbers. If it is not assigned a value, the seed will be a random number

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::Bojarczuk_GP_C(data_train, data_test)
algorithm <- RKEEL::Bojarczuk_GP_C(data_train, data_test, population_size=5, max_generations=10)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

RKEEL documentation built on Sept. 15, 2023, 1:08 a.m.