CPAR-C: CPAR_C KEEL Associative Classification Algorithm

CPAR_CR Documentation

CPAR_C KEEL Associative Classification Algorithm

Description

CPAR_C Associative Classification Algorithm from KEEL.

Usage

CPAR_C(train, test, delta, min_gain, alpha, rules_prediction)

Arguments

train

Train dataset as a data.frame object

test

Test dataset as a data.frame object

delta

delta. Default value = 0.05

min_gain

min_gain. Default value = 0.7

alpha

alpha. Default value = 0.66

rules_prediction

rules_prediction. Default value = 5

Value

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

Examples

data <- loadKeelDataset("breast")

#Create algorithm
algorithm <- RKEEL::CPAR_C(data, data)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

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

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