Identifiyng and solving Concept Drift detection with two approaches
Data can change over time. This can result in poor and degrading predictive performance in predictive models that assume a static relationship between input and output variables. This problem of the changing underlying relationships in the data is called concept drift in the field of machine learning. In the project, I have detected concept drift by using adversarial validation and Kolmogorov-Smirnov test which can also be used in the deployed system.