Verteilungsverschiebung ist ein Phänomen in maschinellem Lernen and künstliche Intelligenz where the statistical properties of the input data change between the training phase and the Inferenzphase. This can occur due to various factors, such as changes in the environment, user behavior, or other external influences that alter the distribution of data.
For example, a model trained on historical sales data may perform well when making predictions in a stable economic environment. However, if a sudden economic downturn occurs, the new data may not reflect the same patterns as the training data, leading to a decline in Modellleistung. This shift can happen in various forms, including Kovariatenverschiebung, where the input features change, and Labelverschiebung, where the distribution of output labels changes.
Verteilungsverschiebung stellt erhebliche Herausforderungen dar, um die Robustheit und Zuverlässigkeit of AI systems. To mitigate its effects, practitioners often employ techniques such as Domänenanpassung, where the model is retrained on new data, or Domänen-Generalisierung, where the model is designed to perform well across various data distributions without needing retraining.
Das Verständnis und die Bewältigung der Verteilungsverschiebung sind entscheidend, um sicherzustellen, dass KI-Modelle remain effective and accurate when deployed in real-world scenarios, where data conditions can frequently change.