Model Drift
Model drift, also known as concept drift, refers to a phenomenon in machine learning where the distribution of the data changes over time, and the trained model’s performance degrades. This happens when the relationship between the input features and the target variable changes – causing the model to make incorrect predictions.
For example, consider a model trained to predict the performance of a football team based on its historical data. Over time, the team managers and players might change, causing the relationship between its team data and to change. In this case, the model trained on the historical data might not perform well on new data and would require retraining to maintain its accuracy.
Model drift can decrease accuracy, false predictions, and incorrect decisions. To prevent model drift, it is important to continuously monitor the performance of a model and retrain it as necessary. Techniques such as drift detection can be used to detect model drift.