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https://issues.apache.org/jira/browse/SPARK-7443?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-7443:
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Description:
TODO: create JIRAs for each task and assign them accordingly.
h2. API
* Check API compliance using java-compliance-checker (SPARK-7458)
* Audit new public APIs (from the generated html doc)
** Scala (do not forget to check the object doc) (SPARK-7537)
** Java compatibility (SPARK-7529)
** Python API coverage (SPARK-7536)
* audit Pipeline APIs (SPARK-7535)
* graduate spark.ml from alpha
** remove AlphaComponent annotations
** remove mima excludes for spark.ml
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)
* Bernoulli naive Bayes (SPARK-7453)
* PIC (SPARK-7454)
* ALS.recommendAll (SPARK-7457)
* perf-tests in Python (SPARK-7539)
*Correctness*
* PMML
** scoring using PMML evaluator vs. MLlib models
* model save/load
h2. Documentation and example code
* Create JIRAs for the user guide to each new algorithm and assign them to the
corresponding author
* Create example code for major components
** cross validation in python
** pipeline with complex feature transformations (scala/java/python)
** elastic-net (possibly with cross validation)
was:
TODO: create JIRAs for each task and assign them accordingly.
h2. API
* Check API compliance using java-compliance-checker (SPARK-7458)
* Audit new public APIs (from the generated html doc)
** Scala (do not forget to check the object doc) (SPARK-7537)
** Java compatibility (SPARK-7529)
** Python API coverage (SPARK-7536)
* audit Pipeline APIs (SPARK-7535)
* graduate spark.ml from alpha
** remove AlphaComponent annotations
** remove mima excludes for spark.ml
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)
* Bernoulli naive Bayes (SPARK-7453)
* PIC (SPARK-7454)
* ALS.recommendAll (SPARK-7457)
* perf-tests in Python
*Correctness*
* PMML
** scoring using PMML evaluator vs. MLlib models
* model save/load
h2. Documentation and example code
* Create JIRAs for the user guide to each new algorithm and assign them to the
corresponding author
* Create example code for major components
** cross validation in python
** pipeline with complex feature transformations (scala/java/python)
** elastic-net (possibly with cross validation)
> MLlib 1.4 QA plan
> -----------------
>
> Key: SPARK-7443
> URL: https://issues.apache.org/jira/browse/SPARK-7443
> Project: Spark
> Issue Type: Umbrella
> Components: ML, MLlib
> Affects Versions: 1.4.0
> Reporter: Xiangrui Meng
> Assignee: Joseph K. Bradley
> Priority: Critical
>
> TODO: create JIRAs for each task and assign them accordingly.
> h2. API
> * Check API compliance using java-compliance-checker (SPARK-7458)
> * Audit new public APIs (from the generated html doc)
> ** Scala (do not forget to check the object doc) (SPARK-7537)
> ** Java compatibility (SPARK-7529)
> ** Python API coverage (SPARK-7536)
> * audit Pipeline APIs (SPARK-7535)
> * graduate spark.ml from alpha
> ** remove AlphaComponent annotations
> ** remove mima excludes for spark.ml
> 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)
> * Bernoulli naive Bayes (SPARK-7453)
> * PIC (SPARK-7454)
> * ALS.recommendAll (SPARK-7457)
> * perf-tests in Python (SPARK-7539)
> *Correctness*
> * PMML
> ** scoring using PMML evaluator vs. MLlib models
> * model save/load
> h2. Documentation and example code
> * Create JIRAs for the user guide to each new algorithm and assign them to
> the corresponding author
> * Create example code for major components
> ** cross validation in python
> ** pipeline with complex feature transformations (scala/java/python)
> ** elastic-net (possibly with cross validation)
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