Interpretable Machine Learning :A Guide For Making Black Box Models Explainable

By:

Molnar C

Language: English Publication details: SPD 2024Edition: 2ndDescription: 317ISBN:

9789355428370

Summary: Contents Preface by the Author ix 1 Introduction 1 1.1 Story Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 What Is Machine Learning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Interpretability 13 2.1 Importance of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Taxonomy of Interpretability Methods . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Scope of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4 Evaluation of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5 Properties of Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.6 Human-friendly Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 Datasets 31 3.1 Bike Rentals (Regression) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.2 YouTube Spam Comments (Text Classification) . . . . . . . . . . . . . . . . . 32 3.3 Risk Factors for Cervical Cancer (Classification) . . . . . . . . . . . . . . . . 33 4 Interpretable Models 35 4.1 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.3 GLM, GAM and more . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.4 Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.5 Decision Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.6 RuleFit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.7 Other Interpretable Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5 Model-Agnostic Methods 109 6 Example-Based Explanations 113 7 Global Model-Agnostic Methods 115 7.1 Partial Dependence Plot (PDP) . . . . . . . . . . . . . . . . . . . . . . . . . . 116 7.2 Accumulated Local Effects (ALE) Plot . . . . . . . . . . . . . . . . . . . . . . 122 7.3 Feature Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 7.4 Functional Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 v vi Contents 7.5 Permutation Feature Importance . . . . . . . . . . . . . . . . . . . . . . . . . 157 7.6 Global Surrogate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 7.7 Prototypes and Criticisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 8 Local Model-Agnostic Methods 179 8.1 Individual Conditional Expectation (ICE) . . . . . . . . . . . . . . . . . . . . 180 8.2 Local Surrogate (LIME) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 8.3 Counterfactual Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 8.4 Scoped Rules (Anchors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 8.5 Shapley Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 8.6 SHAP (SHapley Additive exPlanations) . . . . . . . . . . . . . . . . . . . . . 227 9 Neural Network Interpretation 241 9.1 Learned Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 9.2 Pixel Attribution (Saliency Maps) . . . . . . . . . . . . . . . . . . . . . . . . 254 9.3 Detecting Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 9.4 Adversarial Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 9.5 Influential Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 10 A Look into the Crystal Ball 295 10.1 The Future of Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . 296 10.2 The Future of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 11 Contribute to the Book 301 12 Citing this Book 303 13 Translations 305 14 Acknowledgements 307

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Books	Cummins College of Engineering for Women Pune				006.31 MOL (Browse shelf(Opens below))					Available (not for issue)			CCEP-BK-67497

Browsing Cummins College of Engineering for Women Pune shelves Close shelf browser (Hides shelf browser)

Previous		No cover image available No cover image available						Next
Previous	006.31 KUM Machine Learning In Cognitive IoT	006.31 MAJ Mastering Classification Algorithms For Machine Learning	006.31 MAL Machine Learning With Python: A Hands-On Introduction	006.31 MOL Interpretable Machine Learning :A Guide For Making Black Box Models Explainable	006.31 MOR AI And Machine Learning For Coders : A Programmer's Guide To Artificial Intelligence	006.31 MUL Introduction To Machine Learning With Python Guide For Data Scientists	006.31 MUR Machine Learning : Theory And Practice	Next

Contents
Preface by the Author ix
1 Introduction 1
1.1 Story Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 What Is Machine Learning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Interpretability 13
2.1 Importance of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Taxonomy of Interpretability Methods . . . . . . . . . . . . . . . . . . . . . . 18
2.3 Scope of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4 Evaluation of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.5 Properties of Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.6 Human-friendly Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 Datasets 31
3.1 Bike Rentals (Regression) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 YouTube Spam Comments (Text Classification) . . . . . . . . . . . . . . . . . 32
3.3 Risk Factors for Cervical Cancer (Classification) . . . . . . . . . . . . . . . . 33
4 Interpretable Models 35
4.1 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.3 GLM, GAM and more . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.4 Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.5 Decision Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.6 RuleFit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.7 Other Interpretable Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5 Model-Agnostic Methods 109
6 Example-Based Explanations 113
7 Global Model-Agnostic Methods 115
7.1 Partial Dependence Plot (PDP) . . . . . . . . . . . . . . . . . . . . . . . . . . 116
7.2 Accumulated Local Effects (ALE) Plot . . . . . . . . . . . . . . . . . . . . . . 122
7.3 Feature Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
7.4 Functional Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
v
vi Contents
7.5 Permutation Feature Importance . . . . . . . . . . . . . . . . . . . . . . . . . 157
7.6 Global Surrogate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
7.7 Prototypes and Criticisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
8 Local Model-Agnostic Methods 179
8.1 Individual Conditional Expectation (ICE) . . . . . . . . . . . . . . . . . . . . 180
8.2 Local Surrogate (LIME) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
8.3 Counterfactual Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
8.4 Scoped Rules (Anchors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
8.5 Shapley Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
8.6 SHAP (SHapley Additive exPlanations) . . . . . . . . . . . . . . . . . . . . . 227
9 Neural Network Interpretation 241
9.1 Learned Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
9.2 Pixel Attribution (Saliency Maps) . . . . . . . . . . . . . . . . . . . . . . . . 254
9.3 Detecting Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
9.4 Adversarial Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270
9.5 Influential Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
10 A Look into the Crystal Ball 295
10.1 The Future of Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . 296
10.2 The Future of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
11 Contribute to the Book 301
12 Citing this Book 303
13 Translations 305
14 Acknowledgements 307

There are no comments on this title.

to post a comment.