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  #1  
Old 08-19-2014, 11:04 AM
KMoff KMoff is offline
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Default Linear Regression Classification HW2 Q5/6

Hello, I am having trouble understanding the procedure for binary classification using linear regression.

For ordinary linear regression, as I understand it, we may compute the weights by taking the product pseudo-inverse matrix and the y-vector. In 2D, the line obtained by linear regression is then y = w0 + w1 * x.

Now for the binary case, instead of using the actual y-coordinate value of a data point, we use its binary classification relative to the target function. In this case, the only difference would be that the y-vector which is to be multiplied by the pseudo-inverse matrix consists only of +1 and -1 values.

However, when I try this I get an hypothesis line nearly perpendicular to the target function.

Could someone please clarify? Thanks
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  #2  
Old 08-19-2014, 01:53 PM
KMoff KMoff is offline
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Default Re: Linear Regression Classification HW2 Q5/6

On further investigation. I now think that the y-vector should be the vector whose elements are sign(wf[0] + wf[1] * x) where the target function is y = wf[0] + wf[1] * x. I now have an E_in of about 0.13.
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Old 10-18-2015, 08:10 PM
sandeeps sandeeps is offline
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Default Re: Linear Regression Classification HW2 Q5/6

I get average E_in as ~ 0.13, however the answer is shown as [Answer edited out by admin].
What I have done:
  • Generated a random line (target function)
  • Generated 100 random points; xn
  • Computed yn using target function
  • Computed w using the linear regression equation
  • Computed Ein = mean(h(x) != yn) i.e. number of values incorrectly estimated by w
  • Repeated the above steps 1000 times and averaged Ein

What have I done wrong?
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  #4  
Old 10-18-2015, 10:53 PM
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yaser yaser is offline
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Default Re: Linear Regression Classification HW2 Q5/6

Quote:
Originally Posted by sandeeps View Post
I get average E_in as ~ 0.13, however the answer is shown as [Answer edited out by admin].
What I have done:
  • Generated a random line (target function)
  • Generated 100 random points; xn
  • Computed yn using target function
  • Computed w using the linear regression equation
  • Computed Ein = mean(h(x) != yn) i.e. number of values incorrectly estimated by w
  • Repeated the above steps 1000 times and averaged Ein

What have I done wrong?
The steps seem fine. Can you visually check if the hypothesis line makes sense in a few of the runs?

BTW, if you want to discuss specific answers (chosen or excluded), you need to do so in a thread whose title starts with the warning *ANSWER* per the announcement above.
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  #5  
Old 10-19-2015, 12:48 AM
sandeeps sandeeps is offline
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Default Re: Linear Regression Classification HW2 Q5/6

Sorry for posting the answer in the comment.
I had a bug in the target function which was causing erroneous partitioning. Once I fixed it, things started working as expected. Thank you!
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