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https://issues.apache.org/jira/browse/FLINK-2116?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14571009#comment-14571009
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ASF GitHub Bot commented on FLINK-2116:
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Github user sachingoel0101 commented on the pull request:
https://github.com/apache/flink/pull/772#issuecomment-108482112
Great. This is exactly what I had in mind.
There is perhaps another feature we could incorporate. Every algorithm has
some performance measure to so it can be evaluated on a test data set. We could
incorporate this as a parameter in the model. As soon as evaluate gets called,
this parameter is set to the performance value. It could be squared-error for
MLR, or F-score and accuracy, etc. for SVM, and so on.
User accesses this performance measure with a simple instance.get and (most
likely) prints it, so we don't need to make it of the same type across
different algorithms. Every Predictor can have its own performance object.
> Make pipeline extension require less coding
> -------------------------------------------
>
> Key: FLINK-2116
> URL: https://issues.apache.org/jira/browse/FLINK-2116
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Mikio Braun
> Assignee: Till Rohrmann
> Priority: Minor
>
> Right now, implementing methods from the pipelines for new types, or even
> adding new methods to pipelines requires many steps:
> 1) implementing methods for new types
> implement implicit of the corresponding class encapsulating the operation
> in the companion object
> 2) adding methods to the pipeline
> - adding a method
> - adding a trait for the operation
> - implement implicit in the companion object
> These are all objects which contain many generic parameters, so reducing the
> work would be great.
> The goal should be that you can really focus on the code to add, and have as
> little boilerplate code as possible.
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