MLP-BP-R: MLP_BP_R KEEL Regression Algorithm

In RKEEL: Using 'KEEL' in R Code

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

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