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https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14611768#comment-14611768
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ASF GitHub Bot commented on FLINK-2157:
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Github user thvasilo commented on the pull request:
https://github.com/apache/flink/pull/871#issuecomment-117992843
Changed the approach slightly after Till's suggestion, it is now possible
to have the score function for chained predictors, without needing a new
operation.
Note that we are assuming that Predictors are supervised learning
algorithms in the way score is defined currently. That will have to change once
unsupervised learning algorithms like KMeans clustering are merged.
> Create evaluation framework for ML library
> ------------------------------------------
>
> Key: FLINK-2157
> URL: https://issues.apache.org/jira/browse/FLINK-2157
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Theodore Vasiloudis
> Labels: ML
> Fix For: 0.10
>
>
> Currently, FlinkML lacks means to evaluate the performance of trained models.
> It would be great to add some {{Evaluators}} which can calculate some score
> based on the information about true and predicted labels. This could also be
> used for the cross validation to choose the right hyper parameters.
> Possible scores could be F score [1], zero-one-loss score, etc.
> Resources
> [1] [http://en.wikipedia.org/wiki/F1_score]
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