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 7:52 AM, Aakash Basu <aakash.spark.raj@gmail.com> wrote:
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 code chunk:
paramGrid = ParamGridBuilder() \
.addGrid(rf.numTrees, [50, 100, 150, 200]) \
.addGrid(rf.maxDepth, [5, 10, 15, 20]) \
.addGrid(rf.minInfoGain, [0.001, 0.01, 0.1, 0.6]) \
.addGrid(rf.minInstancesPerNode, [5, 15, 30, 50, 100]) \
.build()

tvs = TrainValidationSplit(estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=MulticlassClassificationEvaluator(),
# 80% of the data will be used for training, 20% for validation.
trainRatio=0.8)

model = tvs.fit(trainingData)

predictions = model.transform(testData)

evaluator = MulticlassClassificationEvaluator(
labelCol="label", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Accuracy = %g" % accuracy)
print("Test Error = %g" % (1.0 - accuracy))

Any help?


Thanks,
Aakash.