Github user staple commented on the pull request:
https://github.com/apache/spark/pull/2362#issuecomment-55636095
@davies understood, thanks for the feedback. It sounds like for now the
preference is to continue caching the python serialized version because the
reduced memory footprint is currently worth the cpu cost of repeated
deserialization.
Would it make sense to preserve the portions of this patch that drop
caching for the NaiveBayes, ALS, and DecisionTree learners, which I do not
believe require external caching to prevent repeated RDD re-evaluation during
learning? NavieBayes only evaluates its input RDD once, while ALS and
DecisionTree internally persist transformations of their input RDDs.
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