Hello:

I'm having some doubts with these questions as well. For questions 1 and 2, I trained on the first 25 samples of in.dta, validated on the last 10 samples of the same data file, and finally evaluated Eout for each of the five models with the 250 samples in out.dta.

The five models are linear regression models with 4 to 7 weigths using the first 4 through all seven nonlinear transformations explained in the question. As before (with homework 6), once a linear model predicts a certain Y value, the sign operation is taken on this sum

to see how each point is classified. The classification error is simply the ration of misclassified points in the entire test/validation data set.

I know I must be doing something wrong because when I see question 5, my Eout's are much higher than any of the options given. After all, when you have a 4 to 7 parameter model and you're only training with 10 or 25 samples, you'd expect an outrageous Eout. ... What is the mistake in my procedure?