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Posted to issues@spark.apache.org by "Vincent (JIRA)" <ji...@apache.org> on 2016/08/15 02:34:20 UTC

[jira] [Created] (SPARK-17055) add labelKFold to CrossValidator

Vincent created SPARK-17055:
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             Summary: add labelKFold to CrossValidator
                 Key: SPARK-17055
                 URL: https://issues.apache.org/jira/browse/SPARK-17055
             Project: Spark
          Issue Type: New Feature
          Components: MLlib
    Affects Versions: 2.0.0
            Reporter: Vincent
            Priority: Minor


Current CrossValidator only supports k-fold, which randomly divides all the samples in k groups of samples. But in cases when data is gathered from different subjects and we want to avoid over-fitting, we want to hold out samples with certain labels from training data and put them into validation fold, i.e. we want to ensure that the same label is not in both testing and training sets.

Mainstream package like Sklearn already supports such cross validation method. 



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