These Jiras' are still unresolved: https://issues.apache.org/jira/browse/SPARK-11215
Also there is https://issues.apache.org/jira/browse/SPARK-8418 On Wed, Aug 17, 2016 at 11:15 AM, Nisha Muktewar <ni...@cloudera.com> wrote: > > The OneHotEncoder does *not* accept multiple columns. > > You can use Michal's suggestion where he uses Pipeline to set the stages > and then executes them. > > The other option is to write a function that performs one hot encoding on > a column and returns a dataframe with the encoded column and then call it > multiple times for the rest of the columns. > > > > > On Wed, Aug 17, 2016 at 10:59 AM, janardhan shetty <janardhan...@gmail.com > > wrote: > >> I had already tried this way : >> >> scala> val featureCols = Array("category","newone") >> featureCols: Array[String] = Array(category, newone) >> >> scala> val indexer = new StringIndexer().setInputCol(fe >> atureCols).setOutputCol("categoryIndex").fit(df1) >> <console>:29: error: type mismatch; >> found : Array[String] >> required: String >> val indexer = new StringIndexer().setInputCol(fe >> atureCols).setOutputCol("categoryIndex").fit(df1) >> >> >> On Wed, Aug 17, 2016 at 10:56 AM, Nisha Muktewar <ni...@cloudera.com> >> wrote: >> >>> I don't think it does. From the documentation: >>> https://spark.apache.org/docs/2.0.0-preview/ml-features.html >>> #onehotencoder, I see that it still accepts one column at a time. >>> >>> On Wed, Aug 17, 2016 at 10:18 AM, janardhan shetty < >>> janardhan...@gmail.com> wrote: >>> >>>> 2.0: >>>> >>>> One hot encoding currently accepts single input column is there a way >>>> to include multiple columns ? >>>> >>> >>> >> >
