Przemyslaw Biecek, Tomasz Burzykowski

Explanatory Model Analysis (eBook, PDF)

Explore, Explain, and Examine Predictive Models

• Format: PDF

Produktbeschreibung

This book is about a new field in statistical machine learning - about interpretation and explanation of predictive models. Machine learning models are widely used in predictive modelling, both for regression and classification.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 324
• Erscheinungstermin: 17. März 2021
• Englisch
• ISBN-13: 9780429651373
• Artikelnr.: 60859819

Autorenporträt

Przemyslaw Biecek is a professor in human-oriented machine learning at the Warsaw University of Technology and Principal Data Scientist in Samsung R&D Institute Poland. His main research project is DrWhy.AI - tools and methods for exploration, explanation, visualisation, and debugging of predictive models.

Tomasz Burzykowski is professor of biostatistics at Hasselt University and Vice-President for Research at International Drug Development Institute (IDDI). He has published extensively on applications of statistics in medicine and biology.

Inhaltsangabe

I. Introduction 1. Introduction. 2. Model Development. 3. Do
it
yourself. 4. Datasets and models. II. Instance Level. 5. Introduction to Instance
level Exploration. 6. Break
down Plots for Additive Attributions. 7. Break
down Plots for Interactions. 8. Shapley Additive Explanations (SHAP) for Average Attributions. 9. Local Interpretable Model
agnostic Explanations (LIME). 10. Ceteris
paribus Profiles. 11. Ceteris
paribus Oscillations. 12. Local
diagnostics Plots. 13. Summary of Instance
level Exploration. III. Dataset Level. 14. Introduction to Dataset
level Exploration. 15. Model
performance Measures. 16. Variable
importance Measures. 17. Partial
dependence Profiles. 18. Local
dependence and Accumulated
dependence Profiles. 19. Residual Diagnostics Plots. 20. Summary of Model
level Exploration. IV. Use
cases. 21. FIFA 19.