[SPARK-13545] [MLlib] [PySpark] Make MLlib LogisticRegressionWithLBFGS's default parameters consistent in Scala and Python #11424
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yanboliang
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Test build #52170 has finished for PR 11424 at commit
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Thanks. Merged into master. |
roygao94
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Mar 22, 2016
…s default parameters consistent in Scala and Python ## 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 <[email protected]> Closes apache#11424 from yanboliang/spark-13545.
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What changes were proposed in this pull request?
regParamof PySpark MLlibLogisticRegressionWithLBFGSshould be consistent with Scala which is0.0. (This is also consistent with MLLogisticRegression.)LogisticRegressionWithLBFGSwill call the ML implementation. We should update the API doc to clarifyingnumCorrectionswill have no effect if we fall into that route.LogisticRegressionWithLBFGS, others are set properly.cc @mengxr @dbtsai
How was this patch tested?
No new tests, it should pass all current tests.