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Lately, I am seeing both terms used interchangeably in several scenarios.

Are there differences in the definitions? Especially between data shift and distribution shift?

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I'm not aware of a precise and accepted definition of each of these terms which sharply distinguishes them. There is an excellent blog post on the topic here. But broadly speaking:

  • Model drift: This refers to the general idea that in some cases model predictions deteriorate over time. I.e. the distribution of model predictions and distribution of true values drift apart from each other. This can happen for a number of reasons.

    • Concept Drift: This is drift due to the dependent variable. The distributions of data might be staying the same but the relationship between input and output has been altered. For example, in a model to detect fraudulent activity, there may be a change in the definition of what is considered fraudulent.
    • Data Drift: This is due to changes in the distributions of the input data. For example, using the fraud example again, we might see an increase in certain types of fraud which change the distributions of observations from what was seen in the training data.
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  • $\begingroup$ So concept drift referes to the Y, and data drift to the X? $\endgroup$ Oct 19, 2021 at 17:21
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    $\begingroup$ I would say concept drift refers more to a change in the relationship between X and Y. $\endgroup$
    – Adam Kells
    Oct 19, 2021 at 18:09

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