I just wanted to complete Holden’s answer. Since Spark 2.3.0,OneHotEncoder has been deprecated and it will be removed in 3.0.0. Please use OneHotEncoderEstimator instead. In Scala: import org.apache.spark.ml.Pipeline import org.apache.spark.ml.feature.{OneHotEncoderEstimator, StringIndexer} val df = Seq((0, “a”, 1), (1, “b”, 2), (2, “c”, 3), (3, “a”, 4), (4, “a”, 4), (5, “c”, 3)).toDF(“id”, “category1”, “category2”) val … Read more
Starting with Spark 1.0 there are two methods you can use to solve this easily: RDD.zipWithIndex is just like Seq.zipWithIndex, it adds contiguous (Long) numbers. This needs to count the elements in each partition first, so your input will be evaluated twice. Cache your input RDD if you want to use this. RDD.zipWithUniqueId also gives … Read more
o.a.s.mllib contains old RDD-based API while o.a.s.ml contains new API build around Dataset and ML Pipelines. ml and mllib reached feature parity in 2.0.0 and mllib is slowly being deprecated (this already happened in case of linear regression) and most likely will be removed in the next major release. So unless your goal is backward … Read more
The main difference will come from underlying frameworks. In case of Mahout it is Hadoop MapReduce and in case of MLib it is Spark. To be more specific – from the difference in per job overhead If your ML algorithm mapped to the single MR job – main difference will be only startup overhead, which … Read more