Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.
Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.
Discusses the concept of data dredging in machine learning and its risks.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.
