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Sean Owen commented on SPARK-17055: ----------------------------------- Hm, when would you want a label to not be present in training but present in test or vice versa? they should, in general, have the same distribution. > add labelKFold to CrossValidator > -------------------------------- > > Key: SPARK-17055 > URL: https://issues.apache.org/jira/browse/SPARK-17055 > Project: Spark > Issue Type: New Feature > Components: MLlib > 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 packages like Sklearn already supports such cross validation > method. > (http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.LabelKFold.html#sklearn.cross_validation.LabelKFold) -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org