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From "Lee Dongjin (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-4591) Algorithm/model parity for spark.ml (Scala)
Date Thu, 14 Jun 2018 08:33:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-4591?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16512153#comment-16512153
] 

Lee Dongjin commented on SPARK-4591:
------------------------------------

[~josephkb] Excuse me. By SPARK-14376 was resolved recently, I think we should make this issue
be resolve also.

> 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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