Dados de Distribuição Interna is a term usada em aprendizado de máquina and inteligência artificial to describe data that is drawn from the same distribution as the dataset used to train a model. This concept is crucial for evaluating the performance and reliability of modelos de IA, as they are typically designed to make predictions based on the patterns learned from their dados de treinamento.
When a model is trained, it learns to recognize patterns, features, and relationships within the training dataset. In-distribution data helps ensure that the model’s predictions remain accurate and relevant. For instance, if a model is trained on images of cats and dogs from a specific set of environments, it is expected to perform well when presented with new images of cats and dogs from similar environments—that is, the in-distribution data.
Por outro lado, dados que estão fora da distribuição de treinamento são chamados de fora de distribuição (OOD) data. Models often struggle with out-of-distribution data because they have not encountered these scenarios during training. As a result, the predictions made on OOD data may be less reliable, leading to potential errors or misclassifications.
Understanding the distinction between in-distribution and out-of-distribution data is vital for AI practitioners, as it influences model evaluation, robustness, and generalization capabilities. Techniques such as adaptação de domínio or aprendizado por transferência are often employed to melhorar o desempenho do modelo em dados OOD, reduzindo a lacuna entre diferentes distribuições de dados.