There are methods for converting the dataframe based random forest models to the old RDD based models and vice versa. Perhaps using these will help given that the old models can be saved and loaded?
In order to use them however you will need to write code in the org.apache.spark.ml package. I've not actually tried doing this myself but it looks as if it might work. Regards, James On 11 April 2016 at 10:29, Ashic Mahtab <as...@live.com> wrote: > Hello, > I'm trying to save a pipeline with a random forest classifier. If I try to > save the pipeline, it complains that the classifier is not Writable, and > indeed the classifier itself doesn't have a write function. There's a pull > request that's been merged that enables this for Spark 2.0 (any dates > around when that'll release?). I am, however, using the Spark Cassandra > Connector which doesn't seem to be able to create a CqlContext with spark > 2.0 snapshot builds. Seeing that ML Lib's random forest classifier supports > storing and loading models, is there a way to create a Spark ML pipeline in > Spark 1.6 with a random forest classifier that'll allow me to store and > load the model? The model takes significant amount of time to train, and I > really don't want to have to train it every time my application launches. > > Thanks, > Ashic. >
