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https://issues.apache.org/jira/browse/FLINK-1723?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14566352#comment-14566352
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Sachin Goel commented on FLINK-1723:
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One major use of cross-validation is to determine model parameters while
training. It would be prudent to provide a framework for specifying which model
parameters are to be varied and what various values of those parameters should
be used to train the model. After this, the training phase would simply proceed
as usual, essentially running the fit function for all combinations of
parameters and keeping only the best for the Prediction phase.
> Add cross validation for parameter selection and validation
> -----------------------------------------------------------
>
> Key: FLINK-1723
> URL: https://issues.apache.org/jira/browse/FLINK-1723
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Mikio Braun
> Labels: ML
>
> Cross validation [1] 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.
> Resources:
> [1] [http://en.wikipedia.org/wiki/Cross-validation]
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