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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2157) Create evaluation framework for ML library
Date Wed, 08 Jul 2015 12:07:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14618493#comment-14618493

ASF GitHub Bot commented on FLINK-2157:

Github user tillrohrmann commented on a diff in the pull request:

    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/recommendation/ALS.scala
    @@ -25,11 +25,15 @@ import org.apache.flink.api.scala._
     import org.apache.flink.api.common.operators.Order
     import org.apache.flink.core.memory.{DataOutputView, DataInputView}
     import org.apache.flink.ml.common._
    -import org.apache.flink.ml.pipeline.{FitOperation, PredictDataSetOperation, Predictor}
    +import org.apache.flink.ml.evaluation.RegressionScores
    +import org.apache.flink.ml.math.{DenseVector, BLAS}
    +import org.apache.flink.ml.pipeline._
     import org.apache.flink.types.Value
     import org.apache.flink.util.Collector
    -import org.apache.flink.api.common.functions.{Partitioner => FlinkPartitioner, GroupReduceFunction,
    +import org.apache.flink.api.common.functions.{Partitioner => FlinkPartitioner,
    +  GroupReduceFunction, CoGroupFunction}
    +// TODO: Use only one BLAS interface
    --- End diff --
    You're right. Let's open a separate PR for this.

> 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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