Explanations
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Interpretable Machine Learning
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Explaining Explanation
This second edition of David-Hillel Ruben's influential and highly acclaimed book on the philosophy of explanation has been revised and expanded, and the author has made substantial changes in light of the extensive reviews the first edition received. Ruben's views on the place of laws in explanation has been refined and clarified. What is perhaps the central thesis of the book, his realist view of explanation, describing the way in which explanation depends on metaphysics, has been updated and extended and engages with some of the work in this area published since the book's first edition.
Understanding, Explanation, and Scientific Knowledge
Author: Kareem Khalifa
language: en
Publisher: Cambridge University Press
Release Date: 2017-10-05
The first comprehensive exploration of the nature and value of understanding, addressing burgeoning debates in epistemology and philosophy of science.