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Business, 19.10.2021 14:00 rickyop8010

Suppose a company has hourly readings from 800 sensors for a whole year that are used to monitor theworkings of a large system of industrial equipment. Given some labeled anomalies, an engineer wants to build a classifier to predict anomalies from future readings of these sensors. She trains a machinelearning model and uses 10-fold cross-validation to tune hyper-parameters (using vanilla methods from scikit-learn, for example). When new data comes in, however, the accuracy of the classifier is poor. She is confused as the cross-validation error was quite good. What might explain these results?

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