Hi Aakash,
First you will want to get the the random forest model stage from the best
pipeline model result, for example if RF is the first stage:
rfModel = model.bestModel.stages[0]
Then you can check the values of the params you tuned like this:
rfModel.getNumTrees
On Mon, Apr 16, 2018 at 7:
Hi,
I am running a Random Forest model on a dataset using hyper parameter
tuning with Spark's paramGrid and Train Validation Split.
Can anyone tell me how to get the best set for all the four parameters?
I used:
model.bestModel()
model.metrics()
But none of them seem to work.
Below is the c
