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https://issues.apache.org/jira/browse/SPARK-13448?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yanbo Liang resolved SPARK-13448.
---------------------------------
Resolution: Fixed
Fix Version/s: 2.0.0
> Document MLlib behavior changes in Spark 2.0
> --------------------------------------------
>
> Key: SPARK-13448
> URL: https://issues.apache.org/jira/browse/SPARK-13448
> Project: Spark
> Issue Type: Documentation
> Components: ML, MLlib
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
> Priority: Blocker
> Fix For: 2.0.0
>
>
> This JIRA keeps a list of MLlib behavior changes in Spark 2.0. So we can
> remember to add them to the migration guide / release notes.
> * SPARK-13429: change convergenceTol in LogisticRegressionWithLBFGS from 1e-4
> to 1e-6.
> * SPARK-7780: Intercept will not be regularized if users train binary
> classification model with L1/L2 Updater by LogisticRegressionWithLBFGS,
> because it calls ML LogisticRegresson implementation. Meanwhile if users set
> without regularization, training with or without feature scaling will return
> the same solution by the same convergence rate(because they run the same code
> route), this behavior is different from the old API.
> * SPARK-12363: Bug fix for PowerIterationClustering which will likely change
> results
> * SPARK-13048: LDA using the EM optimizer will keep the last checkpoint by
> default, if checkpointing is being used.
> * SPARK-12153: Word2Vec now respects sentence boundaries. Previously, it did
> not handle them correctly.
> * SPARK-10574: HashingTF uses MurmurHash3 by default in both spark.ml and
> spark.mllib
> * SPARK-14768: Remove expectedType arg for PySpark Param
> * SPARK-14931: Mismatched default Param values between pipelines in Spark and
> PySpark
> * SPARK-13600: QuantileDiscretizer now uses approxQuantile from DataFrame
> stats (previously used custom sampling logic). Buckets will differ for same
> input data and params.
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