Assignment 4.knwf (138.0 KB)

It is for the Neural Network, and I ned to figure out the code for the R part.

I need help with the RNN part. Thank you so much

## Neural Networks

#install.packages(neuralnet)

require(neuralnet)

## Create two classification variables

df$yes ← c(df$Y == “1”)

df$no ← c(df$Y == “0”)

## Partition the data into training and validation sets

## 70% of the sample size

smp_size ← floor(0.70 * nrow(df))

## set the seed to make the partition reproducible

set.seed(123)

train_ind ← sample(c(1:nrow(df)), size = smp_size)

train ← df[train_ind, ]

validation ← df[-train_ind, ]

## Train the ANN

nn ← neuralnet(yes + no ~ X1 + X2 + X3 + X4 + X5,

train, hidden=c(5), linear.output = FALSE)

plot(nn, rep=“best”)

summary(nn)

## Predict Y for the validation dataset

predict ← compute(nn, validation[,1:5])$net.result

## Extract predicted values on validation set and construct a confusion matrix

net.prediction ← c(“1”,“0”)[apply(predict,1,which.max)]

predict.table ← table(validation$Y, net.prediction)

predict.table

## Alternatively see more diagnostics with a confusion matrix function

#require(caret)

#require(e1071)

#confusionMatrix(predict.table)

#dim(validation)

predict.df ← as.data.frame(predict)

names(predict.df) ← c(“p_1”, “p_0”)

p_validation ← cbind(validation, predict.df, net.prediction)

knime.out ← p_validation