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 ?
>>>
>>
>>
>
