We don't handle missing value imputation in the current version of MLlib. In future releases, we can store feature information in the dataset metadata, which may store the default value to replace missing values. But no one is committed to work on this feature. For now, you can filter out examples containing missing values and use the rest for training. -Xiangrui
On Tue, Sep 30, 2014 at 11:26 AM, Sameer Tilak <ssti...@live.com> wrote: > Hi All, > Can someone please me to the documentation that describes how missing value > imputation is done in MLLib. Also, any information of how this fits in the > overall roadmap will be great. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
