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https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15251880#comment-15251880
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ASF GitHub Bot commented on FLINK-2157:
---------------------------------------
Github user rawkintrevo commented on the pull request:
https://github.com/apache/flink/pull/1849#issuecomment-212916215
np, also RE: my comment on the docs- I think I can lend a hand there (I was
actually testing functionality to make sure I understood how it worked). Let me
know if I can be of assistance.
Also, I did some more hacking this morning...
```scala
%flink
import org.apache.flink.api.scala._
import org.apache.flink.ml.preprocessing.StandardScaler
val scaler = StandardScaler()//MinMaxScaler()
import org.apache.flink.ml.evaluation.{RegressionScores, Scorer}
val loss = RegressionScores.squaredLoss
val scorer = new Scorer(loss)
import org.apache.flink.ml.regression.MultipleLinearRegression
val mlr = MultipleLinearRegression()
.setIterations(microIters)
.setConvergenceThreshold(0.001)
.setWarmStart(true)
val pipeline = scaler.chainPredictor(mlr)
val evaluationDS = survivalLV.map(x => (x.vector, x.label))
pipeline.fit(survivalLV)
//pipeline.evaluate(survivalLV).collect()
scorer.evaluate(evaluationDS, pipeline).collect().head
```
This throws the `breeze.linalg...` error. So I'm not sure exactly what is
different, but it would seem the breeze.linalg is close to the heart of the
problem(?)
> 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: 1.0.0
>
>
> 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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