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I'm working with a client that needs probability calibration in Dataiku. You can learn about probability calibration from the sklearn documentation. Basically, I need to instantiate an object of the class sklearn.calibration.CalibratedClassifierCV from the classifier trained in Dataiku.
My understanding is that the only way to do this is by creating a custom Python model. This is not the best option since the users are not Python developers and are only using Dataiku through the visual machine learning interface.
Did I miss another way to modify a classifier in Dataiku once it is trained?
It's a true that as of current version of DSS (4.2), you need to use a Custom Python model in the machine learning interface to leverage sklearn probability calibration model.
Even if the users are not Python developers, it is just a matter of using 4 lines of code if the Custom Python model editor, for instance:
from sklearn.ensemble import AdaBoostClassifier
from sklearn.calibration import CalibratedClassifierCV
c = AdaBoostClassifier(n_estimators=20)
clf = CalibratedClassifierCV(c, cv=2, method='isotonic')
Full screen shot:
Note that it can be used as a snippet of code to be reused and adapted easily by your client.