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https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14618359#comment-14618359
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ASF GitHub Bot commented on FLINK-2157:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/871#discussion_r34134390
--- Diff:
flink-staging/flink-ml/src/test/scala/org/apache/flink/ml/regression/MultipleLinearRegressionITSuite.scala
---
@@ -132,4 +132,28 @@ class MultipleLinearRegressionITSuite
absoluteErrorSum should be < 50.0
}
+
+ it should "calculate its score correctly" in {
+ val env = ExecutionEnvironment.getExecutionEnvironment
+ val expectedR2 = 0.29310994289260195
+
+ val mlr = MultipleLinearRegression()
+
+ import RegressionData._
+
+ val parameters = ParameterMap()
+
+ parameters.add(MultipleLinearRegression.Stepsize, 10.0)
+ parameters.add(MultipleLinearRegression.Iterations, 100)
+// parameters.add(MultipleLinearRegression.ConvergenceThreshold, 0.0001)
--- End diff --
Why is it commented out?
> 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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