title: Is Random Forest accuracy related to the number of partitions in Spark?
body: <p>In Spark, is there any relationship between the accuracy of the Random Forest classifier and the number of partitions that the training data occupied?</p>
  
  <p>For example, we store the training data on 10 partitions and train a RandomForest classifier (RF1). then we repartition the training data on 20 partitions and train the second RandomForest classifier (RF2).
  Is there a technical difference between RF1 and RF2?</p>