Deslocamento de distribuição é um fenômeno em aprendizado de máquina and inteligência artificial where the statistical properties of the input data change between the training phase and the fase de inferência. 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 desempenho do modelo. This shift can happen in various forms, including deslocamento de covariáveis, where the input features change, and deslocamento de rótulos, where the distribution of output labels changes.
O deslocamento de distribuição apresenta desafios significativos na manutenção do robustez e confiabilidade of AI systems. To mitigate its effects, practitioners often employ techniques such as adaptação de domínio, where the model is retrained on new data, or generalização de domínio, where the model is designed to perform well across various data distributions without needing retraining.
Compreender e abordar o deslocamento de distribuição é crucial para garantir que modelos de IA remain effective and accurate when deployed in real-world scenarios, where data conditions can frequently change.