Concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways.
With reference to the classic house price prediction use case:
House prices change over time thus the model I use today could make no sense in the future.
What is the best approach to address concept drift?
- Do we keep updating the input replacing older house prices of yesteryear?
- Do we add an extra feature for Date of Sale - by including a temporal aspect as a feature with larger data sets?
- Do we eventually change model hyperparameters during training to build a model that fits better the new data?