MLP_BP_R {RKEEL}

MLP_BP_R KEEL Regression Algorithm

Description

MLP_BP_R Regression Algorithm from KEEL.

Usage

MLP_BP_R(train, test, hidden_layers, hidden_nodes, transfer,
   eta, alpha, lambda, test_data, validation_data,
   cross_validation, cycles, improve, tipify_inputs,
   save_all, seed)

Arguments

train

Train dataset as a data.frame object

test

Test dataset as a data.frame object

hidden_layers

hidden_layers. Default value = 2

hidden_nodes

hidden_nodes. Default value = 15

transfer

transfer. Default value = "Htan"

eta

eta. Default value = 0.15

alpha

alpha. Default value = 0.1

lambda

lambda. Default value = 0.0

test_data

test_data. Default value = TRUE

validation_data

validation_data. Default value = FALSE

cross_validation

cross_validation. Default value = FALSE

cycles

cycles. Default value = 10000

improve

improve. Default value = 0.01

tipify_inputs

tipify_inputs. Default value = TRUE

save_all

save_all. Default value = FALSE

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 values for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("autoMPG6_train")
data_test <- RKEEL::loadKeelDataset("autoMPG6_test")

#Create algorithm
algorithm <- RKEEL::MLP_BP_R(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions