The old mllib API will use RandomForest.trainClassifier() to train a
RandomForestModel;
the new mllib API (AKA ML) will use RandomForestClassifier.train() to train
a RandomForestClassificationModel.
They will produce the same result for a given dataset.
2015-07-31 1:34 GMT+08:00 Bryan Cutler cutl...@gmail.com:
Hi Praveen,
In MLLib, the major difference is that RandomForestClassificationModel
makes use of a newer API which utilizes ML pipelines. I can't say for
certain if they will produce the same exact result for a given dataset, but
I believe they should.
Bryan
On Wed, Jul 29, 2015 at 12:14 PM, praveen S mylogi...@gmail.com wrote:
Hi
Wanted to know what is the difference between
RandomForestModel and RandomForestClassificationModel?
in Mlib.. Will they yield the same results for a given dataset?
