model drift

Predictive models can perform well at first, but it’s common that their performance can decrease somewhat over time. For example, once a predictive model is implemented, the related business changes may alter the outcomes that occur, and so the model may need to be adjusted to fit the new reality. The relationships among the variables have changed, and the model has to be updated as well. To ensure the best ROI from predictive models, their performance should be monitored to catch model drift and adjust as required. Automated monitoring tools can help address this concern.