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CT-AI — AI Testing

ISTQB · Topic 5 of 17

ML Data

Data preparation, data-quality issues, data labelling, label noise, dataset bias.

6 lessons240 questions
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What this topic covers

  1. 01AI-specific quality characteristics and fairness testing480m
  2. 02Follow-up test cases and input data testing for ML480m
  3. 03Macro-averaging, micro-averaging, and regression metrics for ML models480m
  4. 04Oracle problem and output quality evaluation for ML data480m
  5. 05Source test cases, specificity, and transparency in AI testing360m
  6. 06Root Mean Squared Error and Adjusted R-squared for ML Models480m

Other topics in CT-AI — AI Testing

Introduction to Artificial Intelligence7 lessonsIntroduction to AI6 lessonsMachine Learning Overview6 lessonsInput Data Testing6 lessonsMachine Learning Model Testing7 lessonsML Functional Performance Metrics6 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