# Tan_GP-C: Tan_GP_C KEEL Classification Algorithm In RKEEL: Using Keel in R Code

## Description

Tan_GP_C Classification Algorithm from KEEL.

## Usage

 ```1 2 3``` ```Tan_GP_C(train, test, population_size, max_generations, max_deriv_size, rec_prob, mut_prob, copy_prob, w1, w2, elitist_prob, support, seed) ```

## Arguments

 `train` Train dataset as a data.frame object `test` Test dataset as a data.frame object `population_size` population_size. Default value = 150 `max_generations` max_generations. Default value = 100 `max_deriv_size` max_deriv_size. Default value = 20 `rec_prob` rec_prob. Default value = 0.8 `mut_prob` mut_prob. Default value = 0.1 `copy_prob` copy_prob. Default value = 0.01 `w1` w1. Default value = 0.7 `w2` w2. Default value = 0.8 `elitist_prob` elitist_prob. Default value = 0.06 `support` support. Default value = 0.03 `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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```data_train <- RKEEL::loadKeelDataset("iris_train") data_test <- RKEEL::loadKeelDataset("iris_test") #Create algorithm #algorithm <- RKEEL::Tan_GP_C(data_train, data_test) algorithm <- RKEEL::Tan_GP_C(data_train, data_test, population_size = 5, max_generations = 10) #Run algorithm algorithm\$run() #See results algorithm\$testPredictions ```

RKEEL documentation built on Aug. 10, 2017, 5:05 p.m.