Amen
> On Nov 13, 2016, at 7:55 PM, janardhan shetty <janardhan...@gmail.com> wrote: > > 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(featureCols).setOutputCol("categoryIndex").fit(df1) >>> <console>:29: error: type mismatch; >>> found : Array[String] >>> required: String >>> val indexer = new >>> StringIndexer().setInputCol(featureCols).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 ? >>>> >>> >> >
