Are there any relevant transformations of one or more predictors that might improve the classification model?

The UC Irvine Machine Learning Repository contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na,Mg,Al,Si,K,Ca,Ba, and Fe. The data can be accessed via: \#install.packages('mlbench') \# if it is required. > library(mlbench) > data(Glass) > str(Glass) 'data.frame': 214 obs. of 10 variables