[jira] [Assigned] (SPARK-12098) Cross validator with multi-arm bandit search

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-12098?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-12098:
------------------------------------

    Assignee:     (was: Apache Spark)

> Cross validator with multi-arm bandit search
> --------------------------------------------
>
>                 Key: SPARK-12098
>                 URL: https://issues.apache.org/jira/browse/SPARK-12098
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML, MLlib
>            Reporter: Xusen Yin
>
> The classic cross-validation requires all inner classifiers iterate to a fixed number of iterations, or until convergence states. It is costly especially in the massive data scenario. According to the paper Non-stochastic Best Arm Identification and Hyperparameter Optimization (http://arxiv.org/pdf/1502.07943v1.pdf), we can see a promising way to reduce the amount of total iterations of cross-validation with multi-armed bandit search.
> The multi-armed bandit search for cross-validation (bandit search for short) requires warm-start of ml algorithms, and fine-grained control of the inner behavior of the corss validator.
> Since there are bunch of algorithms of bandit search to find the best parameter set, we intent to provide only a few of them in the beginning to reduce the test/perf-test work and make it more stable.



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