HW 2 Problem 6
How is this different from problem 5 other than N=1000 and the fact that these simulated 'out of sample' points (E_out) are generated fresh ? I may be missing something but it seems to boil down to running the same program as in problem 5 with N=1000 for 1000 times; can someone clarify please ? thanks

Re: HW 2 Problem 6
It is my understanding that "fresh data" refers to crossvalidation data. Do we then compute Eout using the weights obtained in problem 5? When I do this, Eout < Ein. When I design the weights using the fresh data, Eout is approximately equal to Ein. Does this makes sense?

Re: HW 2 Problem 6
Quote:
The final hypothesis is indeed the one whose weights were determined in Problem 5, where the training took place. 
Re: HW 2 Problem 6
I am confused here , I don't understand what is final hypothesis here.
There are 1000 target function and corresponding 1000 weight vectors/hypothesis in problem 5 . So for problem 6 , 1000 times I generate 1000 outofsample data and then for each weight vector and target function(from problem 5) I evaluate E_out for that outofsample data and finally average them. This is how I have done. I don't see final hypothesis here , what I am missing , any hint Could it be that in problem 5 there is supposed to be only one target function and many insample data ? If so then the final hypothesis/weights could be that produces minimum insample error E_in . Please clarify. Thanks a lot. 
Re: HW 2 Problem 6
Quote:

Re: HW 2 Problem 6
Thanks a lot. The statements about (i) N being the number of 'insample' training data in both problems and (ii) the freshly generated 1000 points being disjoint from the first set clarified the confusion I had.

Re: HW 2 Problem 6
Thanks Professor yaser.

Re: HW 2 Problem 6
When I generate new data and hypothesis for every single run of 1000 (as the problem suggests) I get stable outofsample result close to (slightly greater than) insample error.
When I estimate 1000 different outofsamples for one insample and single hypothesis I get very different average error rates with high variability from 0.01 to 0.13 Why so? 
Re: HW 2 Problem 6
Quote:

All times are GMT 7. The time now is 10:59 AM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2019, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. AbuMostafa, Malik MagdonIsmail, and HsuanTien Lin, and participants in the Learning From Data MOOC by Yaser S. AbuMostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.