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Xiangrui Meng updated SPARK-8971: --------------------------------- Target Version/s: 1.7.0 (was: 1.6.0) > Support balanced class labels when splitting train/cross validation sets > ------------------------------------------------------------------------ > > Key: SPARK-8971 > URL: https://issues.apache.org/jira/browse/SPARK-8971 > Project: Spark > Issue Type: New Feature > Components: ML > Reporter: Feynman Liang > Assignee: Seth Hendrickson > > {{CrossValidator}} and the proposed {{TrainValidatorSplit}} (SPARK-8484) are > Spark classes which partition data into training and evaluation sets for > performing hyperparameter selection via cross validation. > Both methods currently perform the split by randomly sampling the datasets. > However, when class probabilities are highly imbalanced (e.g. detection of > extremely low-frequency events), random sampling may result in cross > validation sets not representative of actual out-of-training performance > (e.g. no positive training examples could be included). > Mainstream R packages like already > [caret|http://topepo.github.io/caret/splitting.html] support splitting the > data based upon the class labels. -- 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