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https://issues.apache.org/jira/browse/FLINK-1723?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Theodore Vasiloudis updated FLINK-1723:
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Description:
Cross validation [1] is a standard tool to select to estimate the test error
for a model. As such it is a crucial tool for every machine learning library.
The cross validation should work with arbitrary learners and error metrics. A
first cross validation strategy it should support is the k-fold cross
validation.
Resources:
[1] [http://en.wikipedia.org/wiki/Cross-validation]
was:
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]
> Add cross validation for model evaluation
> -----------------------------------------
>
> 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 to estimate the test error
> for a model. As such it is a crucial tool for every machine learning library.
> The cross validation should work with arbitrary learners and error metrics. 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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