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Issue #, if available:
Description of changes:
.refit_full('best')
when "best" differs between trainer and predictor (for example, whenWeightedEnsemble
only has 1 base model, and when predictor was fit with_save_bag_folds=False
. Now the"best"
model refit will be the predictor's "best" model to avoid an exception during.predict(data)
.fit(..., _save_bag_folds=False, calibrate=True, refit_full=False)
where no models can infer and calibration is called, resulting in an exception. Now models that can't infer can be calibrated, which after refit_full results in calibrated refit models.By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.