cla_mlp: MLP for classification
In daltoolbox: Leveraging Experiment Lines to Data Analytics

cla_mlp

R Documentation

MLP for classification

Description

Creates a classification object that uses the Multi-Layer Perceptron (MLP) method. It wraps the nnet library.

Usage

cla_mlp(attribute, slevels, size = NULL, decay = 0.1, maxit = 1000)

Arguments

`attribute`	attribute target to model building
`slevels`	possible values for the target classification
`size`	number of nodes that will be used in the hidden layer
`decay`	how quickly it decreases in gradient descent
`maxit`	maximum iterations

Value

a classification object

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_mlp("Species", slevels, size=3, decay=0.03)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

daltoolbox documentation built on May 29, 2024, 1:57 a.m.