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https://issues.apache.org/jira/browse/SPARK-27293?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Bryan Cutler updated SPARK-27293:
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Component/s: ML
> 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: ML, PySpark
> Affects Versions: 2.4.0
> Reporter: Martin Skauen
> Priority: Major
>
> 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:
> {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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