Algorithm to detect Concept Drift

All ML models are designed to do one thing: learning a probability distribution in the form of P(y|X). In other words, they try to learn how to model an outcome 'y' given the input variables 'X'.

This probability distribution, P(y|X), is also called Concept. Therefore, if the Concept changes, the model may become invalid.

But how do we know if there is a new Concept in our data?
Or, more importantly, how do we measure if the new Concept is …

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