zero323 commented on issue #26033: [SPARK-29363][MLLIB] Make o.a.s.regression.Regressor public URL: https://github.com/apache/spark/pull/26033#issuecomment-538723282 > Beside consistency, is there any benefit? @viirya I believe there is (I tried to make this point in the linked JIRA, as well as somewhat related [SPARK-29212](https://issues.apache.org/jira/browse/SPARK-29212)), though I guess I am not that good in pointing that out. `Regressor` and similarly positioned traits have very little purely technical value - from the point of view of simple composition they can be simply replaced by `Prediction` / `PredictionModel`, however, they provide very useful information about *meaning* of the types, that extend them. Let's imagine for a moment I want to build ensemble learners. Clearly one for regression problem will expect different type of partial learners than one for classification problems: ```scala class EnsembleRegressor extends Estimator[EnsembleRegressor] with ... { def setModels(learners: Array[Regressor]: this.type ... } class EnsembleClassifier extends Estimator[EnsembleClassifier] with ... { def setModels(learners: Array[Classifier]): this.type ... } ``` So having these traits public, allows users to write more precise code, without hacks, like putting 3rd party code in `o.a.s`. Obviously benefits rather limited, as dynamic nature of `DataFrame` leaks all over the place, but at the same time there is no harm giving users more precise tools to express their intentions.
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