{"product_id":"interpretable-machine-learning-by-christoph-molnar","title":"Interpretable Machine Learning by Christoph Molnar","description":"\u003cp\u003e\u003cspan\u003eThis book covers a range of interpretability methods, from inherently interpretable models to methods that can make any model interpretable, such as SHAP, LIME and permutation feature importance. It also includes interpretation methods specific to deep neural networks, and discusses why interpretability is important in machine learning. 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?\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"a-text-italic\"\u003e\"What I love about this book is that it starts with the big picture instead of diving immediately into the nitty gritty of the methods (although all of that is there, too).\"\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e– Andrea Farnham, Researcher at Swiss Tropical and Public Health Institute\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eWho the book is for\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003eThis book is essential for machine learning practitioners, data scientists, statisticians, and anyone interested in making their machine learning models interpretable. It will help readers select and apply the appropriate interpretation method for their specific project.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003e\"This one has been a life saver for me to interpret models. ALE plots are just too good!\"\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e– Sai Teja Pasul, Data Scientist at Kohl's\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"a-text-bold\"\u003eYou'll learn about\u003c\/span\u003e\u003c\/p\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eThe concepts of machine leaning interpretability\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eInherently interpretable models\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eMethods to make any machine model interpretable, such as SHAP, LIME and permutation feature importance\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eInterpretation methods specific to deep neural networks\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eWhy interpretability is important and what's behind this concept\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the author\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003eThe author, Christoph Molnar, is an expert in machine learning and statistics, with a Ph.D. in interpretable machine learning.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eOutline\u003c\/span\u003e\u003c\/p\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eAbout the Book\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e1\u003c\/span\u003e\u003cspan\u003e Introduction\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e2\u003c\/span\u003e\u003cspan\u003e Interpretability\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e3\u003c\/span\u003e\u003cspan\u003e Goals of Interpretability\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e4\u003c\/span\u003e\u003cspan\u003e Methods Overview\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e5\u003c\/span\u003e\u003cspan\u003e Data and Models\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e6\u003c\/span\u003e\u003cspan\u003e Interpretable Models\u003c\/span\u003e\u003c\/span\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLinear Regression\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLogistic Regression\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eGLM, GAM and more\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eDecision Tree\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eDecision Rules\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eRuleFit\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eOther Interpretable Models\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e7\u003c\/span\u003e\u003cspan\u003e Local Model-Agnostic Methods\u003c\/span\u003e\u003c\/span\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eCeteris Paribus Plots\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eIndividual Conditional Expectation (ICE)\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLIME\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eCounterfactual Explanations\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eScoped Rules (Anchors)\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eShapley Values\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eSHAP\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e8\u003c\/span\u003e\u003cspan\u003e Global Model-Agnostic Methods\u003c\/span\u003e\u003c\/span\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003ePartial Dependence Plot (PDP)\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eAccumulated Local Effects (ALE) Plot\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eFeature Interaction\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eFunctional Decompositon\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003ePermutation Feature Importance\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLeave One FEature Out (LOFO) Importance)\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eSurrogate Models\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003ePrototypes and Criticisms\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e9\u003c\/span\u003e\u003cspan\u003e Neural Network Interpretation\u003c\/span\u003e\u003c\/span\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLearned Features\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003ePixel Attribution (Saliency Maps)\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eDetecting Concepts\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eAdversarial Examples\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eInfluential Instances\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"a-list-item\"\u003e\u003cspan class=\"a-text-bold\"\u003e10\u003c\/span\u003e\u003cspan\u003e Beyond the Methods\u003c\/span\u003e\u003c\/span\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eEvaluation of Interpetability Methods\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eStory Time\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eThe Future of Interpretability\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Book Lovers BD","offers":[{"title":"Paperback","offer_id":55156191363363,"sku":null,"price":430.0,"currency_code":"BDT","in_stock":true},{"title":"Hardcover","offer_id":55156191396131,"sku":null,"price":560.0,"currency_code":"BDT","in_stock":true},{"title":"eBook","offer_id":55156191428899,"sku":null,"price":150.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0954\/5872\/2083\/files\/rn-image_picker_lib_temp_115030c1-312d-427e-900b-b3e641a66da5.jpg?v=1779867597","url":"https:\/\/www.bookloversbd.shop\/products\/interpretable-machine-learning-by-christoph-molnar","provider":"Book Lovers BD","version":"1.0","type":"link"}