#1




Questions 14: Clarification

#2




Re: Questions 14: Clarification
The transformed space in the case of would be
The model (hypothesis set) would be a linear combination of these coordinates,
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#3




Re: Questions 14: Clarification
thank you!
to beat a dead horse: for Q1&Q2, for each model, train with the 1st 25 points and, using the weights, eval Ein with the last 10 points, and Eout with the out.dta points... for Q3&Q4, rinse/repeat with a different split for training and validation data [p.s., really enjoying the course, and appreciate your time and consideration in the forum!] 
#4




Re: Questions 14: Clarification
Quote:
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#5




Re: Questions 14: Clarification
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? 
#6




Re: Questions 14: Clarification
Please disregard my previous post.
I made a stupid programming error in my code. As I was generating the predictions for each model on the test/validation data sets, I was comparing that output to the wrong "Y" variable. Once corrected, the out of sample errors of questions 2 and 4 are within the options given in question 5. To those who may be struggling with these questions,

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