Hi, I had the same problem but I finally draw these conclusions:
In the 1st one you cite, it is written "linear regression", but since y is defined as being {-1, +1} it's a classification problem so indeed if data is linearly separable it will work very well.
In the 2nd one you cite, y is defined as being in R so this time it is a regression, where linearly separable data doesn't make any difference. Then the dimensions doesn't guarantee anything since we don't know if the data has a linear relation which is the assumption needed for linear regression.
What do you think?
Why "D vs N does not matter at all"? Final 2017 Problem 18
Hi,
We discussed the D>N problem in handout 01c. I don't understand why the answer to Problem 18 says "D vs N does not matter at all".
Thanks!
1
same question especially since it doesn't match the "linear regression can be made to work perfectly" http://oknoname.herokuapp.com/forum/topic/497/linear-separability-for-linear-regression/
1
Hi, I had the same problem but I finally draw these conclusions:
In the 1st one you cite, it is written "linear regression", but since y is defined as being {-1, +1} it's a classification problem so indeed if data is linearly separable it will work very well.
In the 2nd one you cite, y is defined as being in R so this time it is a regression, where linearly separable data doesn't make any difference. Then the dimensions doesn't guarantee anything since we don't know if the data has a linear relation which is the assumption needed for linear regression.
What do you think?
4
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