#1




hw2 q10: noise calculation on Eout?
Hi,
I'm trying to figure out where I went wrong on #10, but I'm not getting it. There's only one place I can think of where I went wrong and I want to see if this is the place because if so, I need an explanation. So I calculated all my Eout points using the weights that I gathered from problem 9. I then calculated how many points were misclassified. I did this by calculating what the real value should be by using the target function. When I did this, I got approximately .032. Now....the only thing I could think of as to where I went wrong was to use the target function that had some noise when producing the output. If I do this, I get something closer to .1. I feel very uncomfortable with this answer, however. When I calculate my Eout, I really only care about what the actual target function should produce, not what a noisy target function produces. Why would I care about what a noisy target function produces? I only care if the value matched what the actual target function should produce without noise, no? To use a real example: Let's say that the function given was the absolute truth for whether I should approve someone for credit. I then got a data set where 5% of the people that should have gotten credit didn't, and 5% of the people who shouldn't have gotten credit did. If I plugged these people into the target function, I would have found those 10% to be misclassified. Now, when I run my Eout, I don't know what the answer is, but because we preconceived this target function (which we wouldn't know in the real world), I can actually check to see how often my hypothesis was correct. If this is where I went wrong, I hope I can get an explanation. If it's not where I went wrong, I'll keep digging, but I hope someone can provide an explanation as to what the program should look like. Thanks! 
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