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-   -   Questions 1-4: Clarification (http://book.caltech.edu/bookforum/showthread.php?t=4298)

 Kekeli 05-18-2013 01:20 PM

Questions 1-4: Clarification

No regularization, just linear regression with transformed input variables...
So the models k=3..7 correspond to

etc.?

 yaser 05-18-2013 01:42 PM

Re: Questions 1-4: Clarification

Quote:
 Originally Posted by Kekeli (Post 10874) So the models k=3..7 correspond to etc.?
The transformed space in the case of would be

The model (hypothesis set) would be a linear combination of these coordinates,

 Kekeli 05-18-2013 02:09 PM

Re: Questions 1-4: Clarification

thank you!
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!]

 yaser 05-18-2013 06:53 PM

Re: Questions 1-4: Clarification

Quote:
 Originally Posted by Kekeli (Post 10876) 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
Correct, except that it is with the last 10 points.

 jlaurentum 05-20-2013 07:01 AM

Re: Questions 1-4: 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?

 jlaurentum 05-20-2013 01:00 PM

Re: Questions 1-4: Clarification