LFD Book Forum  

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

Reply
 
Thread Tools Display Modes
  #11  
Old 08-06-2012, 08:32 AM
magdon's Avatar
magdon magdon is offline
RPI
 
Join Date: Aug 2009
Location: Troy, NY, USA.
Posts: 595
Default Re: bias and variance - definition of g bar

All the numbers you mention below are approximately correct. You can now explicitly compute bias(x) and var(x) in terms of x, mean(a), mean(b), var(a) and var(b) (mean(b)=0):

bias(x)=(\sin(\pi x)-mean(a)x-mean(b))^2

var(x)=E_{a,b}\left[( (a-mean(a))x+b-mean(b))^2\right]

Bias is the average of bias(x) over x; var is the average of var(x) over x. Set mean(b)=0. One can show that

bias=\frac{1}{2}+\frac{mean(a)^2}{3}-\frac{2mean(a)}{\pi}

var=\frac{var(a)}{3}+var(b)

Note: you can also compute the bias and variance via simulation.

Quote:
Originally Posted by the cyclist View Post
I am struggling to replicate the variance of H_1 of Ex. 2.8 in the text. I was able to get the bias correct (and both bias and variance for H_0), as well as getting the related quiz problem correct, so this is really puzzling me.

I'm trying to narrow down where my mistake might be. Can someone please verify whether or not the correct average hypothesis is

g_bar(x) = a_mean * x + b_mean

where

a_mean ~= 0.776

and

b_mean ~= 0.

I plot that, and it does look like the figure in the book.

Also, when I take the standard deviation (over the data sets) of the coefficients a and b, I get

std(a) ~= 1.52
std(b) ~= 0.96

Do those look right? I am truly puzzled here!
__________________
Have faith in probability
Reply With Quote
  #12  
Old 08-06-2012, 12:36 PM
munchkin munchkin is offline
Member
 
Join Date: Jul 2012
Posts: 38
Default Re: bias and variance - definition of g bar

I'm having doubts about the variance value in example 2.8 since it indicates that the root mean square deviation of the test data from the sinusoid line is 1.3= sqrt(1.69). So the magnitude of the average (a*x+b) difference from (a_mean*x+b_mean) evaluated at a given point on the sinusoid is bigger than the root mean square value (.7071) of the sinusoid that generated the data point in the first place? I'm inclined to doubt that.

The mean square deviation between the slope of each generated line and a_mean is larger than 1.69 so at this point I have no idea where that variance number came from.
Reply With Quote
  #13  
Old 08-06-2012, 02:07 PM
yaser's Avatar
yaser yaser is offline
Caltech
 
Join Date: Aug 2009
Location: Pasadena, California, USA
Posts: 1,475
Default Re: bias and variance - definition of g bar

Quote:
Originally Posted by munchkin View Post
I'm having doubts about the variance value in example 2.8 since it indicates that the root mean square deviation of the test data from the sinusoid line is 1.3= sqrt(1.69). So the magnitude of the average (a*x+b) difference from (a_mean*x+b_mean) evaluated at a given point on the sinusoid is bigger than the root mean square value (.7071) of the sinusoid that generated the data point in the first place? I'm inclined to doubt that.
The average is taken over the entire domain [-1,1] so that includes points where the line (which fits the two training points on the sinusoid) diverges significantly from the sinusoid and from the average line. The figures on page 65 illustrate that.
__________________
Where everyone thinks alike, no one thinks very much
Reply With Quote
  #14  
Old 08-06-2012, 09:32 PM
munchkin munchkin is offline
Member
 
Join Date: Jul 2012
Posts: 38
Default Re: bias and variance - definition of g bar

Yes, I can see that on the charts for example 2.8 but those outlying points do not exert an effect (at a given x) for the averaged g(D)[x] calculation I am using. So I am wrong on both counts!

A careful rereading of page 63 has led me to try averaging over the calculated data set g's at an arbitrary (generic?) point x and using that to calculate the variance of g(D)[x]. This seems to be a step in the right direction since the calculated variance is now a function of that arbitrary x point and has a minimum around x=0 just like the chart in the example 2.8 but based on the values at the extremes and in the middle I can't see how my average variance over the domain [-1,1] would be as low as 1.69. We shall see.

Thanks so much for your helpful comments, they are really appreciated and this is a great class even if I am a little dense in absorbing some of the material. Have a great day.
Reply With Quote
  #15  
Old 08-07-2012, 07:10 AM
the cyclist the cyclist is offline
Member
 
Join Date: Jul 2012
Posts: 26
Default Re: bias and variance - definition of g bar

Finally got it. Thanks to magdon for confirming one part of my calculation, so that I did not need to waste time poring over it. Thanks also to yaser for a tip, in another thread, that helped me a lot. It turns out that I was incorrectly reusing the sample dataset to calculate (via simulation) the variance. Instead, I needed to generate a fresh dataset for that.

It's funny how sometimes making mistakes at first leads to a much more solid understanding later!
Reply With Quote
  #16  
Old 08-07-2012, 12:37 PM
munchkin munchkin is offline
Member
 
Join Date: Jul 2012
Posts: 38
Default Re: bias and variance - definition of g bar

I've got it too! Repeatedly evaluate var[ g(D)[x] ] over the entire data set with x ranging from -1 to 1 and average those values to get 1.69 ! ! ! Feeling a sense of real accomplishment here.
Reply With Quote
Reply

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 02:08 PM.


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.