Marginal likelihood is exhaustive leave-p-out cross-validation

The article discusses the concept of marginal likelihood as a comprehensive method for leave-p-out cross-validation in model evaluation. It highlights the advantages of this approach in providing a more thorough assessment of model performance compared to traditional methods. The implications for developers and data scientists in optimizing their models are also explored.