Choice of Error measure for Logistic Regression
From the textbook (page 91), it seems intuitive that the likelyhood function is the hypothesis.
However, for the case of logistic regression in another book, the choice of the likelyhood function is the bernoulli distribution where y_n is {0, 1}. In the case of heart attacks as specified in the book, I could have changed the label of y_n from {1, 1} to {0, 1} and used the bernoulli distribution as the likelyhood function instead. eg. (h(x)^y_n)*(1h(x)^(1y_n) Therefore, what are the factors involved in the consideration of choosing the error measure and what are the pros and cons in this case? 
Re: Choice of Error measure for Logistic Regression
Quote:

Re: Choice of Error measure for Logistic Regression
There are different ways of deriving the crossentropy error function. The book presents a specific way based on the notation of our choice. Indeed other ways (from other notations/assumptions) are possible.
Hope this helps. 
All times are GMT 7. The time now is 11:37 AM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2020, 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.