Explanatory Model Analysis

Explore, Explain, and Examine Predictive Models
1. Auflage 2022
ISBN: 978-0-367-69392-3
Verlag: Chapman and Hall/CRC

Buch, Englisch, 324 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 494 g

Reihe: Chapman & Hall/CRC Data Science Series

Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

Autoren/Hrsg.

Biecek, Przemyslaw

Burzykowski, Tomasz

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik Mathematik Stochastik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

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.

Über Autor(innen)

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.