Hi Ian, You can create a dense vector of you features as follows: - String Index your features - Invoke One Hot Encoding on them, which generates a sparse vector - Now, in case you wish to merge these features, then use VectorAssembler (optional) - After transforming the dataframe to return sparse vector/s (which you may or may not assemble), you can use Vectos.dense(vector.toArray()) on either the individual One Hot features or the assembled sparse vector.
Hope this helps. Cheers, Disha -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Dense-Vectors-outputs-in-feature-engineering-tp27331p27332.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
