GitHub user coderxiang opened a pull request:
https://github.com/apache/spark/pull/10940
[SPARK-13029][ml] fix a logistic regression issue when inputing data has a
column with identical value
This is a bug that appears while fitting a Logistic Regression model with
`.setStandardization(false)` and `setFitIntercept(false)`. If the data matrix
has one column with identical value, the resulting model is not correct.
Specifically, the special column will always get a weight of 0, due to the
special check inside the code. However, the correct solution, which is unique
for L2 logistic regression, usually has non-zero weight.
The fix is to update the special handing logic to make it compatible with
columns with std=0.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/coderxiang/spark dev
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/10940.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 #10940
----
commit 437a285bfa20d9431819bbd9e01faa2622893616
Author: Shuo Xiang <[email protected]>
Date: 2016-01-27T01:25:22Z
handle Logistic regression with column of unique value
commit 09e95a733922988882914cedbe4b0bfc373420dc
Author: Shuo Xiang <[email protected]>
Date: 2016-01-27T01:25:38Z
handle Logistic regression with column of unique value
----
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
