Skip to content
CT-AI — AI Testing

ISTQB · Topic 7 of 17

ML Functional Performance Metrics

Confusion matrix, precision, recall, F1, ROC/AUC. Trade-offs and selection by use case.

6 lessons240 questions
Start for freeFull certification

What this topic covers

  1. 01ML Functional Performance Metrics: Confusion Matrix and Fairness480m
  2. 02Follow-up test cases and invariance under data augmentation360m
  3. 03Macro-averaging and Micro-averaging for ML Metrics360m
  4. 04Oracle Problem and Metamorphic Testing for ML Systems
  5. 05ML Functional Performance Metrics: Accuracy, Specificity, and Transparency480m
  6. 06RMSE, Adjusted R-squared, and Key Classification Metrics480m

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 lessonsMachine Learning 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