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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