I watched the video Estimating Prediction Error and Validation Set Approach and learned how important it is to accurately estimate prediction error in machine learning models. This video emphasized the significance of reserving a validation set to evaluate the model’s performance.

The K-fold Cross-Validation video was quite informative. I now understand how K-fold cross-validation improves the robustness of a model’s performance over a single validation set. I utilized this strategy in Python for a diabetic data, dividing my dataset into ‘K’ subgroups and using each as a validation set while training on the remaining data iteratively. I found the ‘Cross-Validation: The Right and Wrong Ways’ video to be quite informative. It stressed the correct and incorrect methods of performing cross-validation, shining light on typical blunders to avoid.