[jira] [Updated] (SPARK-27293) Setting random seed produces different results in RandomForestRegressor

["Bryan Cutler (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-27293?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Bryan Cutler updated SPARK-27293:
---------------------------------
    Summary: Setting random seed produces different results in RandomForestRegressor  (was: 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. )

> Setting random seed produces different results in RandomForestRegressor
> -----------------------------------------------------------------------
>
>                 Key: SPARK-27293
>                 URL: https://issues.apache.org/jira/browse/SPARK-27293
>             Project: Spark
>          Issue Type: Question
>          Components: PySpark
>    Affects Versions: 2.4.0
>            Reporter: Martin Skauen
>            Priority: Major
>
> I am calculating the RMSE metric like this:
> {code:java}
> (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)
> {code}
> 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?



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