Understanding how to use nnet in R

Question

This is my first attempt using a machine learning paradigm in R. I'm using a planet data set (url: https://www.kaggle.com/mrisdal/open-exoplanet-catalogue ) and I simply want to predict a planet's size based on the size of its Sun. This is the code I currently have, using nnet():

library(nnet)
#Organize data:
cols_to_keep = c(1,4,21)
full_data <- na.omit(read.csv('Planet_Data.csv')[, cols_to_keep])

#Split data:
train_data <- full_data[sample(nrow(full_data), round(nrow(full_data)/2)),]
rownames(train_data) <- 1:nrow(train_data)
test_data <- full_data[!rownames(full_data) %in% rownames(data1),]
rownames(test_data) <- 1:nrow(test_data)

#nnet
nnet_attempt <- nnet(RadiusJpt~HostStarRadiusSlrRad, data=train_data, size=0, linout=TRUE, skip=TRUE, maxNWts=10000, trace=FALSE, maxit=1000, decay=.001)

nnet_newdata <- predict(nnet_attempt, newdata=test_data)
nnet_newdata

When I print nnet_newdata I get a value for each row in my data, but I don't really understand what these values mean. Is this a proper way to use the nnet() package to predict a simple regression?

Thanks

Answer 1

When predict is called for an object with class nnet you will get, by default, the raw output from the nnet model applied to your new dataset. If, instead, yours is a classification problem, you can use type = "class" .

See here .