d81a71357e
## What changes were proposed in this pull request? * The default value of ```regParam``` of PySpark MLlib ```LogisticRegressionWithLBFGS``` should be consistent with Scala which is ```0.0```. (This is also consistent with ML ```LogisticRegression```.) * BTW, if we use a known updater(L1 or L2) for binary classification, ```LogisticRegressionWithLBFGS``` will call the ML implementation. We should update the API doc to clarifying ```numCorrections``` will have no effect if we fall into that route. * Make a pass for all parameters of ```LogisticRegressionWithLBFGS```, others are set properly. cc mengxr dbtsai ## How was this patch tested? No new tests, it should pass all current tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11424 from yanboliang/spark-13545. |
||
|---|---|---|
| .. | ||
| linalg | ||
| stat | ||
| __init__.py | ||
| classification.py | ||
| clustering.py | ||
| common.py | ||
| evaluation.py | ||
| feature.py | ||
| fpm.py | ||
| random.py | ||
| recommendation.py | ||
| regression.py | ||
| tests.py | ||
| tree.py | ||
| util.py | ||