GitHub user iyerr3 opened a pull request:
https://github.com/apache/incubator-madlib/pull/43
SVM: Add class weights for use with unbalanced data
JIRA: MADLIB-998
Added 'class_weight' in the 'params' argument. It can either be a string or
a
dictionary-like mapping. In case of a string, we currently only accept
'balanced' as an option. For a mapping, the user can map values of the
dependent variable to specific double precision weights. Since, SVM only
supports binary classification at present, the class_weight mapping can only
take upto two arguments.
As part of this work, we add a 'tuple_weight' argument to the SVM aggregate.
This allows future addition of sample weights (which would be multiplied
with
the class weight).
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/iyerr3/incubator-madlib
feature/svm_class_weights
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/incubator-madlib/pull/43.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 #43
----
commit 545bf14c46d1ae5e75088e83abed1c26a841d4f7
Author: Rahul Iyer <[email protected]>
Date: 2016-05-20T23:28:23Z
SVM: Add class weights for use with unbalanced data
JIRA: MADLIB-998
Added 'class_weight' in the 'params' argument. It can either be a string or
a
dictionary-like mapping. In case of a string, we currently only accept
'balanced' as an option. For a mapping, the user can map values of the
dependent variable to specific double precision weights. Since, SVM only
supports binary classification at present, the class_weight mapping can only
take upto two arguments.
As part of this work, we add a 'tuple_weight' argument to the SVM aggregate.
This allows future addition of sample weights (which would be multiplied
with
the class weight).
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