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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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Nick Pentreath updated SPARK-13448:
-----------------------------------
Description:
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: Use approxQuantile from DataFrame stats in QuantileDiscretizer
was:
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
> 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
>
> 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: Use approxQuantile from DataFrame stats in QuantileDiscretizer
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