Przemyslaw Biecek, Tomasz Burzykowski

Explanatory Model Analysis (eBook, PDF)

Explore, Explain, and Examine Predictive Models

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis eBooks
• 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.