Validation is the process of assessing how well a [[machine learning model]] performs on data that it hasn't seen during the training process. The primary purpose of validation is to tune the [[hyperparameter|hyperparameters]] of the model and to make decisions about its architecture. Here's how the validation process works: The validation process starts with [[dataset split]], followed by [[hyperparameter tuning]] together with [[performance assessment]]. [[training]] < [[Hands-on LLMs]]/[[1 Machine Learning Basics]] > [[testing]]