Etikettförskjutning

Etikettförskjutning, also known as label shift or concept drift in machine learning, refers to a phenomenon where the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This change can occur after a model has been trained and deployed. For instance, if a model is trained to predict customer purchasing behavior based on historical data, an etikettförskjutning might happen if new marketing strategies or external economic factors significantly alter what customers are likely to buy.

The core issue with etikettförskjutning is that a model trained on past data may no longer accurately reflect the current reality. The relationship between the input features and the output label evolves, rendering the model's predictions less reliable. This can lead to a degradation in performance, such as decreased accuracy, precision, or recall. Detecting and addressing etikettförskjutning is crucial for maintaining the effectiveness of machine learning systems in dynamic environments.

Several strategies can be employed to mitigate the impact of etikettförskjutning. One common approach is continuous monitoring of the model's performance in production. If a significant drop in accuracy is observed, it can signal the presence of drift. Another method involves retraining the model periodically with updated data that reflects the current distribution of the target variable. Advanced techniques also exist for detecting and adapting to drift in real-time, allowing the model to adjust its predictions dynamically. Understanding and managing etikettförskjutning is an ongoing challenge in the practical application of machine learning.