[SPARK-27293] Setting random seed produces different results in RandomForestRegressor

• Type: Question
• Status: Resolved
• Priority: Major
• Resolution: Not A Problem
• Affects Version/s: 2.4.0
• Component/s: ML, PySpark
• Assignee: Unassigned
• Reporter: Martin Skauen
• Created: 27/Mar/19 11:10
• Updated: 29/Mar/19 00:34
• Resolved: 29/Mar/19 00:34

Description

I am interested in finding out if there is a bug in the implementation of RandomForests. The Issue is when applying a seed and getting different results than other people from my class when applying it to the same data

I am calculating the RMSE metric like this:

(trainingData, testData) = data.randomSplit([0.7, 0.3], 313)
from pyspark.ml.regression import RandomForestRegressor
rfr = RandomForestRegressor(labelCol="labels", featuresCol="features", maxDepth=5, numTrees=3, seed = 313)
from pyspark.ml.evaluation import RegressionEvaluator
evaluator = RegressionEvaluator\
(labelCol="labels", predictionCol="prediction", metricName="rmse")
rmse = evaluator.evaluate(predictions)
print("RMSE = %g " % rmse)

I am setting the seed. For seed = 50 and also for other seeds I get exact same RMSE as people from class. I set seed to 313 and it is giving me different value. What could be the issue here?