I think the spark.ml logistic regression currently only supports 0/1 labels. If you need multiclass, I would suggest to look at either the spark.ml decision trees. If you don't care too much for pipelines, then you could use the spark.mllib logistic regression after featurizing.
On Sat, Aug 29, 2015 at 10:49 PM, Zsombor Egyed <egye...@starschema.net> wrote: > Thank you, I saw this before, but it is "just" a binary classification, so > how can I extract this to multiple classification. > > Simply add different labels? > e.g.: > > new LabeledDocument(0L, "a b c d e spark", 1.0), > new LabeledDocument(1L, "b d", 0.0), > new LabeledDocument(2L, "hadoop f g h", 2.0), > > > > > On Sun, Aug 30, 2015 at 7:32 AM, Feynman Liang <fli...@databricks.com> > wrote: > >> I would check out the Pipeline code example >> <https://spark.apache.org/docs/latest/ml-guide.html#example-pipeline> >> >> On Sat, Aug 29, 2015 at 9:23 PM, Zsombor Egyed <egye...@starschema.net> >> wrote: >> >>> Hi! >>> >>> I want to implement a multiclass classification for documents. >>> So I have different kinds of text files, and I want to classificate them >>> with spark mllib in java. >>> >>> Do you have any code examples? >>> >>> Thanks! >>> >>> -- >>> >>> >>> *Egyed Zsombor * >>> Junior Big Data Engineer >>> >>> >>> >>> Mobile: +36 70 320 65 81 | Twitter:@starschemaltd >>> >>> Email: egye...@starschema.net <bali...@starschema.net> | Web: >>> www.starschema.net >>> >>> >> > > > -- > > > *Egyed Zsombor * > Junior Big Data Engineer > > > > Mobile: +36 70 320 65 81 | Twitter:@starschemaltd > > Email: egye...@starschema.net <bali...@starschema.net> | Web: > www.starschema.net > >
