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| author | Yanbo Liang <ybliang8@gmail.com> | 2016-08-10 10:53:48 -0700 |
|---|---|---|
| committer | Shivaram Venkataraman <shivaram@cs.berkeley.edu> | 2016-08-10 10:53:48 -0700 |
| commit | d4a9122430d6c3aeaaee32aa09d314016ff6ddc7 (patch) | |
| tree | 19e191c481ca385c3fa93b62b5b573c44e5b637c /docs/running-on-yarn.md | |
| parent | 19af298bb6d264adcf02f6f84c8dc1542b408507 (diff) | |
| download | spark-d4a9122430d6c3aeaaee32aa09d314016ff6ddc7.tar.gz spark-d4a9122430d6c3aeaaee32aa09d314016ff6ddc7.tar.bz2 spark-d4a9122430d6c3aeaaee32aa09d314016ff6ddc7.zip | |
[SPARK-16710][SPARKR][ML] spark.glm should support weightCol
## What changes were proposed in this pull request?
Training GLMs on weighted dataset is very important use cases, but it is not supported by SparkR currently. Users can pass argument ```weights``` to specify the weights vector in native R. For ```spark.glm```, we can pass in the ```weightCol``` which is consistent with MLlib.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #14346 from yanboliang/spark-16710.
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