This article explores various metrics used to evaluate the performance of classification machine learning models, including precision, recall, F1-score, accuracy, and alert rate. It explains how these metrics are calculated and provides insights into their application in real-world scenarios, particularly in fraud detection.
A ready-to-run tutorial in Python and scikit-learn to evaluate a classification model compared to a baseline model
