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https://issues.apache.org/jira/browse/SPARK-9568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-9568:
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Description:
h2. API
* Check binary API compatibility (SPARK-9658)
* Audit new public APIs (from the generated html doc)
** Scala (SPARK-9660)
** Java compatibility (SPARK-9661)
** Python coverage (SPARK-9662)
* Check Experimental, DeveloperApi tags (SPARK-9665)
h2. Algorithms and performance
*Performance*
* _List any other missing performance tests from spark-perf here_
* LDA online/EM (SPARK-7455)
* ElasticNet for linear regression and logistic regression (SPARK-7456)
* PIC (SPARK-7454)
* ALS.recommendAll (SPARK-7457)
* perf-tests in Python (SPARK-7539)
*Correctness*
* model save/load (SPARK-9666)
h2. Documentation and example code
* For new algorithms, create JIRAs for updating the user guide (SPARK-9668)
* For major components, create JIRAs for example code (SPARK-9670)
* Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671)
was:
h2. API
* Check binary API compatibility
* Audit new public APIs (from the generated html doc)
** Scala
** Java compatibility
** Python coverage
* Check Experimental, DeveloperApi tags
h2. Algorithms and performance
*Performance*
* _List any other missing performance tests from spark-perf here_
* LDA online/EM (SPARK-7455)
* ElasticNet for linear regression and logistic regression (SPARK-7456)
* PIC (SPARK-7454)
* ALS.recommendAll (SPARK-7457)
* perf-tests in Python (SPARK-7539)
*Correctness*
* model save/load (SPARK-9666)
h2. Documentation and example code
* For new algorithms, create JIRAs for updating the user guide (SPARK-9668)
* For major components, create JIRAs for example code (SPARK-9670)
* Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671)
> Spark MLlib 1.5.0 testing umbrella
> ----------------------------------
>
> Key: SPARK-9568
> URL: https://issues.apache.org/jira/browse/SPARK-9568
> Project: Spark
> Issue Type: Umbrella
> Components: MLlib
> Reporter: Reynold Xin
> Assignee: Xiangrui Meng
>
> h2. API
> * Check binary API compatibility (SPARK-9658)
> * Audit new public APIs (from the generated html doc)
> ** Scala (SPARK-9660)
> ** Java compatibility (SPARK-9661)
> ** Python coverage (SPARK-9662)
> * Check Experimental, DeveloperApi tags (SPARK-9665)
> h2. Algorithms and performance
> *Performance*
> * _List any other missing performance tests from spark-perf here_
> * LDA online/EM (SPARK-7455)
> * ElasticNet for linear regression and logistic regression (SPARK-7456)
> * PIC (SPARK-7454)
> * ALS.recommendAll (SPARK-7457)
> * perf-tests in Python (SPARK-7539)
> *Correctness*
> * model save/load (SPARK-9666)
> h2. Documentation and example code
> * For new algorithms, create JIRAs for updating the user guide (SPARK-9668)
> * For major components, create JIRAs for example code (SPARK-9670)
> * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671)
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