When a model is successful and provides accurate predictions, there are instances where it can eventually become completely incorrect due to changing conditions. For instance, a crime prediction model that effectively forecasts crime locations may eventually prompt criminals to modify their behavior, thereby rendering the model inaccurate once again.
Model Decay vs. Concept Drift: do we need to re-label our data to improve accuracy?
Model Decay vs. Concept Drift: do we need to…
Model Decay vs. Concept Drift: do we need to re-label our data to improve accuracy?
When a model is successful and provides accurate predictions, there are instances where it can eventually become completely incorrect due to changing conditions. For instance, a crime prediction model that effectively forecasts crime locations may eventually prompt criminals to modify their behavior, thereby rendering the model inaccurate once again.