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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2259) Support training Estimators using a (train, validation, test) split of the available data
Date Tue, 17 May 2016 11:59:13 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2259?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15286455#comment-15286455
] 

ASF GitHub Bot commented on FLINK-2259:
---------------------------------------

Github user tillrohrmann commented on the pull request:

    https://github.com/apache/flink/pull/1898#issuecomment-219696600
  
    The PR looks really good @rawkintrevo. I had some minor comments. Once they are fixed,
we should be good to merge this PR :-)


> Support training Estimators using a (train, validation, test) split of the available
data
> -----------------------------------------------------------------------------------------
>
>                 Key: FLINK-2259
>                 URL: https://issues.apache.org/jira/browse/FLINK-2259
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Theodore Vasiloudis
>            Assignee: Trevor Grant
>            Priority: Minor
>              Labels: ML
>
> When there is an abundance of data available, a good way to train models is to split
the available data into 3 parts: Train, Validation and Test.
> We use the Train data to train the model, the Validation part is used to estimate the
test error and select hyperparameters, and the Test is used to evaluate the performance of
the model, and assess its generalization [1]
> This is a common approach when training Artificial Neural Networks, and a good strategy
to choose in data-rich environments. Therefore we should have some support of this data-analysis
process in our Estimators.
> [1] Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical
learning. Vol. 1. Springer, Berlin: Springer series in statistics, 2001.



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