GitHub user oefirouz opened a pull request:
https://github.com/apache/spark/pull/5301
[MLLIB] Add fit intercept api to ml logisticregression
I have the fit intercept enabled by default for logistic regression, I
wonder what others think here. I understand that it enables allocation
by default which is undesirable, but one needs to have a very strong
reason for not having an intercept term enabled so it is the safer
default from a statistical sense.
Explicitly modeling the intercept by adding a column of all 1s does not
work. I believe the reason is that since the API for
LogisticRegressionWithLBFGS forces column normalization, and a column of all
1s has 0 variance so dividing by 0 kills it.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/oefirouz/spark addIntercept
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/5301.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #5301
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commit bd9663cc078289b2676c6bbf38a5e973bd5b82cc
Author: Omede Firouz <[email protected]>
Date: 2015-03-31T18:19:59Z
[MLLIB] Add fit intercept api to ml logisticregression
I have the fit intercept enabled by default for logistic regression, I
wonder what others think here. I understand that it enables allocation
by default which is undesirable, but one needs to have a very strong
reason for not having an intercept term enabled so it is the safer
default from a statistical sense.
Explicitly modeling the intercept by adding a column of all 1s does not
work. I believe the reason is that since the API for
LogisticRegressionWithLBFGS forces column normalization, and a column of all
1s has 0 variance so dividing by 0 kills it.
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