Interpretable Machine Learning (Record no. 359833)

MARC details
000 -LEADER
fixed length control field 04460 a2200169 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20241018124752.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241018b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355428370
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
100 ## - MAIN ENTRY--PERSONAL NAME
Author Molnar C.
245 ## - TITLE STATEMENT
Title Interpretable Machine Learning
Remainder of title :A Guide For Making Black Box Models Explainable
250 ## - EDITION STATEMENT
Edition statement 2nd
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. SPD
Date of publication, distribution, etc. 2024
300 ## - PHYSICAL DESCRIPTION
Extent 317
520 ## - SUMMARY, ETC.
Summary, etc. Contents<br/>Preface by the Author ix<br/>1 Introduction 1<br/>1.1 Story Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3<br/>1.2 What Is Machine Learning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9<br/>1.3 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10<br/>2 Interpretability 13<br/>2.1 Importance of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . 13<br/>2.2 Taxonomy of Interpretability Methods . . . . . . . . . . . . . . . . . . . . . . 18<br/>2.3 Scope of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20<br/>2.4 Evaluation of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . 22<br/>2.5 Properties of Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22<br/>2.6 Human-friendly Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br/>3 Datasets 31<br/>3.1 Bike Rentals (Regression) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31<br/>3.2 YouTube Spam Comments (Text Classification) . . . . . . . . . . . . . . . . . 32<br/>3.3 Risk Factors for Cervical Cancer (Classification) . . . . . . . . . . . . . . . . 33<br/>4 Interpretable Models 35<br/>4.1 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37<br/>4.2 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53<br/>4.3 GLM, GAM and more . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59<br/>4.4 Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77<br/>4.5 Decision Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83<br/>4.6 RuleFit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99<br/>4.7 Other Interpretable Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107<br/>5 Model-Agnostic Methods 109<br/>6 Example-Based Explanations 113<br/>7 Global Model-Agnostic Methods 115<br/>7.1 Partial Dependence Plot (PDP) . . . . . . . . . . . . . . . . . . . . . . . . . . 116<br/>7.2 Accumulated Local Effects (ALE) Plot . . . . . . . . . . . . . . . . . . . . . . 122<br/>7.3 Feature Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140<br/>7.4 Functional Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147<br/>v<br/>vi Contents<br/>7.5 Permutation Feature Importance . . . . . . . . . . . . . . . . . . . . . . . . . 157<br/>7.6 Global Surrogate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165<br/>7.7 Prototypes and Criticisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170<br/>8 Local Model-Agnostic Methods 179<br/>8.1 Individual Conditional Expectation (ICE) . . . . . . . . . . . . . . . . . . . . 180<br/>8.2 Local Surrogate (LIME) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185<br/>8.3 Counterfactual Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . 194<br/>8.4 Scoped Rules (Anchors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205<br/>8.5 Shapley Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215<br/>8.6 SHAP (SHapley Additive exPlanations) . . . . . . . . . . . . . . . . . . . . . 227<br/>9 Neural Network Interpretation 241<br/>9.1 Learned Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243<br/>9.2 Pixel Attribution (Saliency Maps) . . . . . . . . . . . . . . . . . . . . . . . . 254<br/>9.3 Detecting Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265<br/>9.4 Adversarial Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270<br/>9.5 Influential Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279<br/>10 A Look into the Crystal Ball 295<br/>10.1 The Future of Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . 296<br/>10.2 The Future of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . 298<br/>11 Contribute to the Book 301<br/>12 Citing this Book 303<br/>13 Translations 305<br/>14 Acknowledgements 307
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Vendor Net Price Total Checkouts Full call number Barcode Date last seen Actual Price Bill Date Koha item type
    Dewey Decimal Classification     Cummins College of Engineering for Women Pune Cummins College of Engineering for Women Pune 11/10/2024 115 1087.50   006.31 MOL CCEP-BK-67497 11/10/2024 1450.00 11/10/2024 Books