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Old 07-23-2012, 01:41 PM
rakhlin rakhlin is offline
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Join Date: Jun 2012
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Default Re: HW 2 Problem 6

Quote:
Originally Posted by yaser View Post
Just to clarify. You used the in-sample points to train and arrived at a final set of weights (corresponding to the final hypothesis). Each out of-sample point is now tested on this hypothesis and compared to the target value on the same point. Now, what exactly do you do to get the two scenarios you are describing?
1-st (normal) scenario: I test out-of-sample data set (100 points) against linear model. I repeat it 1000 times: generate 100 in-sample points, linear fit, generate 100 out-of-sample points, test. On each iteration accumulate # of mistaken points. Average errors when done. Average error is stable from run to run.

2-nd scenario: fit linear model only once. Repeat 1000 times: generate 100 out-of-sample points, test. Accumulate and average errors when done. Here I get remarkable variation in average error.

I'd like to understand why these scenarios different. I believe they must not
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