Training and Testing Games

This lecture focuses on the importance of separating annotated data for training and testing machine learning models. It explains the significance of using distinct datasets for model evaluation, emphasizing the need to avoid mixing training and testing data. The instructor discusses the concepts of training and testing sets, highlighting the critical role of using unseen labeled data for model evaluation. The lecture also covers the validation set and its purpose in model selection. Various strategies for dataset partitioning are presented, including cross-validation techniques to ensure unbiased model evaluation. The instructor stresses the necessity of proper data separation to accurately assess model performance and avoid overfitting.