What's the difference between the ml.classification.DecisionTreeClassifier in ml and the mllib.tree.DecisionTree trainClassifier in mllib?
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The spark.mllib DecisionTree classifier is built on top of RDDs while the spark.ml provides a further API built on top of DataFrames for constructing ML pipelines. It's recommended to use spark.ml due to the advantages of DataFrames optmizations.
Here's a good post I found after searching more of the forums: Difference between org.apache.spark.ml.classification and org.apache.spark.mllib.classification

peterlandis
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