LFD Book Forum  

Go Back   LFD Book Forum > Book Feedback - Learning From Data > Chapter 2 - Training versus Testing

Thread Tools Display Modes
Old 09-21-2016, 03:41 PM
wolszhang wolszhang is offline
Junior Member
Join Date: Sep 2016
Posts: 5
Lightbulb Exercise 2.6

I am pretty confused with the Ein and Eout. According to my understanding, Etest 's error bar is calculated by Hoeffding Bounds with hypothesis set size = 1. But if i want to use Ein to estimate Eout, I either need the growth function or dvc and none of these two is given. i would assume that Etest would have a smaller error bar. But i cannot prove it.
Also, for part(b), the only reason i come up with is that the hypothesis that we would be testing would not be as good as it is with a bigger N. And if Etest is big, we are screwed.
Any help would be appreciated. Thanks
Reply With Quote
Old 10-19-2016, 08:07 AM
wqymcgill wqymcgill is offline
Junior Member
Join Date: Oct 2016
Posts: 1
Default Re: Exercise 2.6


For part(a), I simply assume dvc=0 and get an error bar of about 0.316. Etest's error bar is only about 0.096. So Ein will always be higher thatn Etest no matter what dvc is.

For part(b), I don't know why either.. Hope anyone helps.
Reply With Quote
Old 07-09-2017, 12:04 AM
bonfire09 bonfire09 is offline
Junior Member
Join Date: Jul 2017
Posts: 1
Default Re: Exercise 2.6

I'm stuck on this problem too. I was thinking we just use the generalization error for both.

Do we just calculate E_out<=E_test+sqrt(1/200 ln (2/0.5)) and
E_out<=E_in+sqrt(1/400 ln(2/0.5)) ?
Reply With Quote
Old 09-23-2017, 12:06 PM
SpencerNorris SpencerNorris is offline
Junior Member
Join Date: Sep 2017
Posts: 2
Default Re: Exercise 2.6

I'm wondering what bounds we should use for E_test and E_in. It states that E_test obeys the simple Hoeffding bound; does this mean that we should use the generalization error outlined on p.40, eq. 2.1? Or can we use the VC Bound on E_test as well as E_in?
Reply With Quote
Old 09-23-2017, 07:15 PM
anirban.das anirban.das is offline
Junior Member
Join Date: Aug 2016
Posts: 1
Default Re: Exercise 2.6

2.6.a I think that the training error bound (as well as the testing error bound) is given by equation (2.1), because in this particular case the hypothesis set is finite already.

2.6.b Also, here I suppose more examples in testing data set will do nothing much as it is already a good apprx. to the E_out, but this will decrease the available training samples and we would get a stupid final hypothesis.

Any thoughts?
Reply With Quote
Old 02-22-2018, 06:09 AM
k_sze k_sze is offline
Join Date: Dec 2016
Posts: 12
Default Re: Exercise 2.6

I don't think the growth function m_{\mathcal{H}} or VC dimension d_{vc} are involved at all? The exercise tells us the exact size of the hypothesis set (the learning model), which is 1000, right?
Reply With Quote
Old 03-15-2018, 03:10 AM
seoma seoma is offline
Junior Member
Join Date: Mar 2018
Posts: 1
Default Re: Exercise 2.6

thanks for your post
Reply With Quote

Thread Tools
Display Modes

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is Off

Forum Jump

All times are GMT -7. The time now is 03:15 PM.

Powered by vBulletin® Version 3.8.3
Copyright ©2000 - 2022, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, and participants in the Learning From Data MOOC by Yaser S. Abu-Mostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.