Predicting glass type revisited

In Chapter 3, Logistic Regression, we analyzed the glass identification data set, whose task is to identify the type of glass comprising a glass fragment found at a crime scene. The output of this data set is a factor with several class levels corresponding to different types of glass. Our previous approach was to build a one-versus-all model using multinomial logistic regression. The results were not very promising, and one of the main points of concern was a poor model fit on the training data.

In this section, we will revisit this data set and see whether a neural network model can do better. At the same time, we will demonstrate how neural networks can handle classification problems as well:

> glass <- read.csv("glass.data", ...

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