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Explainable Christoph Molnar  \nThis book is for sale at [http://leanpub.com/interpretable-machine-learning](http://leanpub.com/interpretable-machine-learning)[ ](http://leanpub.com/interpretable-machine-learning)[This version was published on 2019-02-21](This version was published on 2019-02-21)  \nThis is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do.  \nThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4 .0 International License  \nContents  \nPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \nIntroduction .................................................. 2  \nStory Time ................................................. 4  \nWhat Is Machine Learning? ...................................... 10  \nTerminology ................................................ 12  \nInterpretability ................................................ 15  \nImportance of Interpretability ..................................... 15  \nTaxonomy of Interpretability Methods ................................ 21  \nScope of Interpretability ......................................... 23  \nEvaluation of Interpretability ..................................... 25  \nProperties of Explanations ....................................... 26  \nHuman-friendly Explanations ..................................... 29  \nDatasets ..................................................... 34  \nBike Rentals (Regression) ........................................ 34  \nYouTube Spam Comments (Text Classification) .......................... 35  \nRisk Factors for Cervical Cancer (Classification) .......................... 36  \nInterpretable Models ............................................. 37  \nLinear Regression ............................................. 38  \nLogistic Regression ............................................ 54  \nGLM, GAM and more .......................................... 61  \nDecision Tree ............................................... 79  \nDecision Rules .............................................. 85  \nRuleFit ................................................... 100  \nOther Interpretable Models ....................................... 108  \nModel-Agnostic Methods .......................................... 110  \nPartial Dependence Plot (PDP) ..................................... 113  \nIndividual Conditional Expectation (ICE) .............................. 119  \nAccumulated Local Effects (ALE) Plot ................................ 125  \nFeature Interaction ............................................ 146  \nFeature Importance ............................................ 154  \nCONTENTS  \nGlobal Surrogate ............................................. 163  \nLocal Surrogate (LIME) ......................................... 168  \nShapley Values .............................................. 177  \nExample-Based Explanations ....................................... 189  \nCounterfactual Explanations ...................................... 191  \nAdversarial Examples .......................................... 199  \nPrototypes and Criticisms ........................................ 208  \nInfluential Instances ........................................... 218  \nA Look into the Crystal Ball ........................................ 234  \nThe Future of Machine Learning ................................... 235  \nThe Future of Interpretability ..................................... 237  \nContribute to the Book ........................................... 240  \nCiting this Book ................................................ 241  \nAcknowledgements ............................................. 242  \nRefe","cbCaimzAc4HTHOfm","https://ap.wps.com/l/cbCaimzAc4HTHOfm","pdf",3618082,251,"English","# Preface\n# Introduction\n# Story Time\n# What Is Machine Learning?\n# Interpretability\n## Taxonomy of Interpretability Methods\n## Evaluation of Interpretability\n# Interpretable Models\n## Linear Regression\n## Logistic Regression\n## Decision Tree\n## RuleFit\n# Model-Agnostic Methods\n## Partial Dependence Plot (PDP)\n## Individual Conditional Expectation (ICE)\n## Accumulated Local Effects (ALE) Plot\n## Feature Interaction\n# Feature Importance\n## Global Surrogate\n## Local Surrogate (LIME)\n## Shapley Values\n# Example-Based Explanations\n## Counterfactual Explanations\n# Prototypes and Criticisms\n# The Future of Machine Learning","[{\"question\":\"What is the main goal of this book?\",\"answer\":\"The book focuses on making machine learning models and their predictions interpretable, overcoming the barrier that black box models usually do not explain their outputs.\"},{\"question\":\"Which kinds of interpretability methods are covered?\",\"answer\":\"It covers simple interpretable models (e.g., decision trees, decision rules, linear regression) and model-agnostic techniques (e.g., feature importance and accumulated local effects), plus local explanation methods such as Shapley values and LIME.\"},{\"question\":\"What data 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