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https://issues.apache.org/jira/browse/SPARK-4591?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16520635#comment-16520635
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Joseph K. Bradley commented on SPARK-4591:
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There are still a few contained tasks which are incomplete. I'd like to leave
this open for now.
> Algorithm/model parity for spark.ml (Scala)
> -------------------------------------------
>
> Key: SPARK-4591
> URL: https://issues.apache.org/jira/browse/SPARK-4591
> Project: Spark
> Issue Type: Umbrella
> Components: ML
> Reporter: Xiangrui Meng
> Priority: Critical
>
> This is an umbrella JIRA for porting spark.mllib implementations to use the
> DataFrame-based API defined under spark.ml. We want to achieve critical
> feature parity for the next release.
> h3. Instructions for 3 subtask types
> *Review tasks*: detailed review of a subpackage to identify feature gaps
> between spark.mllib and spark.ml.
> * Should be listed as a subtask of this umbrella.
> * Review subtasks cover major algorithm groups. To pick up a review subtask,
> please:
> ** Comment that you are working on it.
> ** Compare the public APIs of spark.ml vs. spark.mllib.
> ** Comment on all missing items within spark.ml: algorithms, models, methods,
> features, etc.
> ** Check for existing JIRAs covering those items. If there is no existing
> JIRA, create one, and link it to your comment.
> *Critical tasks*: higher priority missing features which are required for
> this umbrella JIRA.
> * Should be linked as "requires" links.
> *Other tasks*: lower priority missing features which can be completed after
> the critical tasks.
> * Should be linked as "contains" links.
> h4. Excluded items
> This does *not* include:
> * Python: We can compare Scala vs. Python in spark.ml itself.
> * Moving linalg to spark.ml: [SPARK-13944]
> * Streaming ML: Requires stabilizing some internal APIs of structured
> streaming first
> h3. TODO list
> *Critical issues*
> * [SPARK-14501]: Frequent Pattern Mining
> * [SPARK-14709]: linear SVM
> * [SPARK-15784]: Power Iteration Clustering (PIC)
> *Lower priority issues*
> * Missing methods within algorithms (see Issue Links below)
> * evaluation submodule
> * stat submodule (should probably be covered in DataFrames)
> * Developer-facing submodules:
> ** optimization (including [SPARK-17136])
> ** random, rdd
> ** util
> *To be prioritized*
> * single-instance prediction: [SPARK-10413]
> * pmml [SPARK-11171]
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