A Comparison of Approaches for Handling Concept Drifts in Data Processed With Machine Learning
In the realm of machine learning models, the pursuit of achieving favorable metrics is undeniably significant.However, these models confront phenomena that can diminish their effectiveness if left unaddressed-notably, the phenomenon of concept drift.Concept drift materializes when unforeseen alterations in the statistical properties of the target v