ISTQB · Topic 8 of 17
Machine Learning Functional Performance Metrics
K3 calculation objectives — students must compute metrics (~120 min). Confusion matrix, classification metrics, regression metrics, benchmarks.
6 lessons603 questions
What this topic covers
- 01Confusion Matrix and Classification Metrics360m
- 02Choosing the Right Metric for the Problem360m
- 03Regression Metrics1500m
- 04Benchmarks, Confidence, and Limits of Metrics360m
- 05Chapter Review and Practice Questions (heavy on calculation)480m
- 06Adjusted R-squared and Business Metric Selection360m
Other topics in CT-AI — AI Testing
Introduction to Artificial Intelligence7 lessonsIntroduction to AI6 lessonsMachine Learning Overview6 lessonsInput Data Testing6 lessonsML Data6 lessonsMachine Learning Model Testing7 lessonsML Functional Performance Metrics6 lessonsTesting Generative AI Systems6 lessonsQuality Characteristics for AI-Based Systems4 lessonsTesting AI-Specific Quality Characteristics6 lessonsML Neural Networks and Testing6 lessonsTesting AI-Based Systems Overview6 lessonsTest Environments for AI-Based Systems6 lessonsMachine Learning Development Testing3 lessonsUsing AI for Testing6 lessonsMethods and Techniques for Testing AI6 lessons