LFD Book Forum Problem 1.11
 User Name Remember Me? Password
 Register FAQ Calendar Mark Forums Read

 Thread Tools Display Modes
#1
09-17-2014, 11:35 PM
 LowEntropy Junior Member Join Date: Sep 2014 Posts: 1
Problem 1.11

I guess I'm confused with how to apply the equation from the slides:

Ein = 1/N sum( e( h(x) , f(x) ) )

where e( h(x), f(x) ) == 1 or 0

To the problem with weighted cost values. Where do the weights from each possible choice fall into play in finding the in-sample error for the problem of classification? Thanks!
#2
09-18-2014, 09:41 AM
 magdon RPI Join Date: Aug 2009 Location: Troy, NY, USA. Posts: 595
Re: Problem 1.11

When h=f you would choose e(h,f)=0. When you need to distinguish between the false positive and the false negative and administer an error (penalty) accordingly. That is where the risk matrix comes in. It tells you how to penalize the different errors.

Quote:
 Originally Posted by LowEntropy I guess I'm confused with how to apply the equation from the slides: Ein = 1/N sum( e( h(x) , f(x) ) ) where e( h(x), f(x) ) == 1 or 0 To the problem with weighted cost values. Where do the weights from each possible choice fall into play in finding the in-sample error for the problem of classification? Thanks!
__________________
Have faith in probability

 Thread Tools Display Modes Linear Mode

 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 Rules
 Forum Jump User Control Panel Private Messages Subscriptions Who's Online Search Forums Forums Home General     General Discussion of Machine Learning     Free Additional Material         Dynamic e-Chapters         Dynamic e-Appendices Course Discussions     Online LFD course         General comments on the course         Homework 1         Homework 2         Homework 3         Homework 4         Homework 5         Homework 6         Homework 7         Homework 8         The Final         Create New Homework Problems Book Feedback - Learning From Data     General comments on the book     Chapter 1 - The Learning Problem     Chapter 2 - Training versus Testing     Chapter 3 - The Linear Model     Chapter 4 - Overfitting     Chapter 5 - Three Learning Principles     e-Chapter 6 - Similarity Based Methods     e-Chapter 7 - Neural Networks     e-Chapter 8 - Support Vector Machines     e-Chapter 9 - Learning Aides     Appendix and Notation     e-Appendices

All times are GMT -7. The time now is 11:47 AM.

 Contact Us - LFD Book - Top

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. 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.