This book is about a new field in statistical machine learning â about interpretation and explanation of predictive models. Machine learning models are widely used in predictive modelling, both for regression and classification.
This book is about a new field in statistical machine learning â about interpretation and explanation of predictive models. Machine learning models are widely used in predictive modelling, both for regression and classification.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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