My opinion on todays class.

Resampling methods involve creating new samples from the original data, which are invaluable for inference, model evaluation, and estimating statistics. One such method is bootstrapping, where we generate multiple samples by randomly selecting data points with replacement – often used to estimate population metrics or assess statistic uncertainty, especially with limited data. Cross-validation, another resampling strategy, is common in machine learning. It divides the dataset into subsets, using them iteratively for training and testing, helping gauge model generalization and identify issues like overfitting. Estimating prediction error is crucial to understanding how our model may perform with new, unseen data, and several methods can be employed based on our data and goals.