Model Drift

Model drift, also known as concept drift, refers to the change in data patterns over time that result in the degradation of a predictive model’s performance. In the field of machine learning, predictive models are trained with the assumption that the future will behave similarly to what the model was trained on. The real world is dynamic, and the underlying data distribution can change over time, impacting the accuracy of predictions and potentially making the model obsolete. This change in statistical properties of the target variable or predictors over time is known as model drift.

Model drift can be induced by several factors, including the evolution of technology, changes in customer behavior, seasonal variations, economic shifts, among others. For example, a machine learning model used to predict retail sales could initially be very accurate, but with changes in consumer behavior, market trends, or new product introductions, the previously used factors might no longer apply, and the model’s predictions could become significantly less accurate.

Identifying and handling model drift is crucial to maintain the accuracy of models in production. This involves consistent monitoring of the model’s performance and the statistical properties of the input data. When drift is detected, an update or retraining of the model may be necessary. In some cases, drift can be managed through regular iterative experimentation and model updating, while in others, more proactive, adaptive learning models are required.