Exploring the issues of drift
The most obvious issue of drift is the degradation of the accuracy. However, there are more issues than you might initially notice, which include the following:
- Applicability: The model’s ability to make accurate predictions on new, unseen data may be compromised as data patterns and distributions shift. This can result in reduced effectiveness in real-world scenarios and diminished value for decision-making, which raises the likelihood of the model becoming less relevant and practical to use.
- Interpretability: Understanding and explaining the model’s decisions can become challenging, as the factors influencing its predictions may no longer align with the current data landscape. This can hinder effective communication with stakeholders and impede trust in the model’s predictions. Note that an originally explainable model is still explainable as we can still produce accurate information on how it used the input data, but...
