Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Explains the evaluation of information retrieval models, including recall, precision, F-Measure, and the precision/recall tradeoff.
Discusses the evaluation of binary classifiers, including recall, sensitivity, specificity, ROC curves, and performance measures.
