Description
Description
The ensemble.StackingClassifier (slated for release in v0.22) stack_method="auto" predict-function priority does not match that of CalibratedClassifierCV.
In StackingClassifier, when stack_method="auto" the priority is:
predict_proba,decision_function,predict.
In CalibratedClassifierCV the priority is
decision_function,predict_proba,predict.
Steps/Code to Reproduce
No steps to reproduce. I raise this point for discussion.
Expected Results
I expected scikit's cross-validation routines/techniques (in which I include CalibratedClassifierCV and StackingClassifier) to use the same resolution priority when determining which predict function to invoke among predict_prob, decision_function, and predict.
Actual Results
When stack_method="auto" (the default value) in StackingClassifier the predict_proba method is preferred to decision_function and predict. The docs state:
For StackingClassifier, note that the output of the estimators is controlled by the parameter
stack_method and it is called by each estimator. This parameter is either a string, being estimator
method names, or 'auto' which will automatically identify an available method depending on the
availability, tested in the order of preference: predict_proba, decision_function and predict.
Versions
I am using version 0.22rc2.post1.