Evaluating Machine Learning Classification Models

Learn how to evaluate machine learning classification models with confidence using the principles and guidance of ISO/IEC TS 4213:2022. This course takes you beyond model development to focus on what matters most: determining whether a classifier is accurate, reliable, efficient, and fit for purpose. Drawing on real-world AI applications, you will explore the complete classification assessment process, from understanding data quality, bias, and evaluation design to selecting and interpreting the most appropriate performance metrics. You will learn how to assess binary, multi-class, and multi-label classification models using measures such as accuracy, precision, recall, F-scores, ROC and Precision-Recall curves, AUROC, AUPRC, Hamming Loss, Jaccard Index, and Kullback-Leibler divergence. The course also explores operational considerations including latency, throughput, computational efficiency, and energy consumption, as well as statistical techniques for comparing model performance and validating results. What makes this course unique is its standards-based approach, combining technical evaluation methods, operational performance measures, and statistical significance testing within a single practical framework. By the end of the course, you will be able to evaluate classifiers rigorously, compare competing models objectively, and communicate performance results with greater confidence and credibility.