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Supervised drift detection on the penguins dataset


When true outputs/labels are available, we can perform supervised drift detection; monitoring the model’s performance directly in order to check for harmful drift. Two detectors ideal for this application are the Fisher’s Exact Test (FET) detector and Cramér-von Mises (CVM) detector detectors.

The FET detector is designed for use on binary data, such as the instance level performance indicators from a classifier (i.e. 0/1 for each incorrect/correct classification). The CVM detector is designed use on continuous data, such as a regressor’s instance level loss or error scores.

In this example we will use the offline versions of these detectors, which are suitable for use on batches of data. In many cases data may arrive sequentially, and the user may wish to perform drift detection as the data arrives to ensure it is detected as soon as possible. In this case, the online versions of the FET and CVM detectors can be used, as will be explored in a future example.


The palmerpenguins dataset [HHG20] consists of data on 344 penguins from 3 islands in the Palmer Archipelago, Antarctica. There are 3 different species of penguin in the dataset, and a common task is to classify the the species of each penguin based upon two features, the length and depth of the peguin’s bill, or beak.