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 ?
>>
>
>
