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Communications Mining user guide

Last updated Aug 1, 2025

Concept drift

In predictive analytics and machine learning, the term concept drift, or data drift, means that the properties of the target variables, that is, the themes and concepts underlying each of the labels that the model is trying to predict, change over time in unforeseen ways.

Essentially, more recent data coming into the dataset will become increasingly different over time to the original data on which the model was trained.

This causes problems because the predictions become less accurate, as time passes, and the variables that the model is trying to predict become increasingly different from the training data.

Concept drift is one of the key reasons why it is important to properly maintain models used in production use-cases. For example, automations can be done by performing a small amount of exception training on a scheduled basis.

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