Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/2137#issuecomment-53684027
The assumption is usually unrealistic. For logistic regression, it is
common to have the predictions be something like 0.99999 or 0.000001, and they
cannot be interpreted as probabilities without calibration. Logistic regression
is not responsible for it. I created a JIRA for isotonic regression, which can
be used for calibration: https://issues.apache.org/jira/browse/SPARK-3278
For the method names, my suggestion would be: add `classify` that outputs
classes using a threshold, keep `predict` that output the raw predictions. Do
not distinguish `predictScore` and `predictProb`.
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