El cambio de distribución es un fenómeno en aprendizaje automático and inteligencia artificial where the statistical properties of the input data change between the training phase and the fase de inferencia. 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 rendimiento del modelo. This shift can happen in various forms, including cambio de covariables, where the input features change, and desplazamiento de etiquetas, where the distribution of output labels changes.
El cambio de distribución plantea desafíos importantes para mantener la robustez y fiabilidad of AI systems. To mitigate its effects, practitioners often employ techniques such as adaptación de dominios, where the model is retrained on new data, or generalización de dominio, where the model is designed to perform well across various data distributions without needing retraining.
Comprender y abordar el cambio de distribución es crucial para garantizar que modelos de IA remain effective and accurate when deployed in real-world scenarios, where data conditions can frequently change.