A 4.3.3 Model Evaluation & Hyperparameter Tuning
A4.3.3 explains the role of hyperparameter tuning in optimising supervised learning algorithms. It details crucial evaluation metrics like accuracy, precision, recall, and the F1 score (calculated as the harmonic mean of precision and recall, used to assess models like CNNs). The topic also covers common training issues such as overfitting and underfitting. Hyperparameter tuning helps address these issues to ensure...