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Posted to dev@flink.apache.org by "Till Rohrmann (JIRA)" <ji...@apache.org> on 2015/03/18 12:02:38 UTC

[jira] [Created] (FLINK-1723) Add cross validation for parameter selection and validation

Till Rohrmann created FLINK-1723:
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             Summary: Add cross validation for parameter selection and validation
                 Key: FLINK-1723
                 URL: https://issues.apache.org/jira/browse/FLINK-1723
             Project: Flink
          Issue Type: Improvement
          Components: Machine Learning Library
            Reporter: Till Rohrmann


Cross validation is a standard tool to select proper parameters for you model and to validate your results. As such it is a crucial tool for every machine learning library.

The cross validation should work with arbitrary learners and ranges of parameters you can specify. A first cross validation strategy it should support is the k-fold cross validation.



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