If the missing values are 0, then you can also look into implicit formulation...
On Tue, Sep 30, 2014 at 12:05 PM, Xiangrui Meng <men...@gmail.com> wrote: > 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 > >
