Feature Engineering Selection For Explainable Models
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Feature Engineering Selection For Explainable Models
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Author : Md Azimul Haque
language : en
Publisher:
Release Date : 2022-12-27
Feature Engineering Selection For Explainable Models written by Md Azimul Haque and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-27 with categories.
Learn about 10 percent of things that make 90 percent of the impact in your machine learning project.
Feature Engineering And Selection
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Author : Max Kuhn
language : en
Publisher: CRC Press
Release Date : 2019-07-25
Feature Engineering And Selection written by Max Kuhn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-25 with Business & Economics categories.
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
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.
Explainable Ai With Python
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Author : Leonida Gianfagna
language : en
Publisher: Springer Nature
Release Date : 2021-04-28
Explainable Ai With Python written by Leonida Gianfagna and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-28 with Computers categories.
This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI. Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, showing how intrinsic interpretable models can be interpreted and how to produce “human understandable” explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.” Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.
Deep Learning In Gaming And Animations
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Author : Vikas Chaudhary
language : en
Publisher: CRC Press
Release Date : 2021-12-07
Deep Learning In Gaming And Animations written by Vikas Chaudhary and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.
Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges. This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking you through the fundamental ideas with expert ease. This book progresses in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence (AI), deep learning, and machine learning have changed the world in gaming and animation. It gives us the idea that AI can also be applied in gaming, and there are limited textbooks in this area. This book comprehensively addresses all the aspects of AI and deep learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Deep Learning in Gaming and Animations teaches you how to apply the power of deep learning to build complex reasoning tasks. After being exposed to the foundations of machine and deep learning, you will use Python to build a bot and then teach it the game's rules. This book also focuses on how different technologies have revolutionized gaming and animation with various illustrations.
Explainable Ai Foundations Methodologies And Applications
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Author : Mayuri Mehta
language : en
Publisher: Springer Nature
Release Date : 2022-10-19
Explainable Ai Foundations Methodologies And Applications written by Mayuri Mehta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Technology & Engineering categories.
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
The Art Of Feature Engineering
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Author : Pablo Duboue
language : en
Publisher: Cambridge University Press
Release Date : 2020-06-25
The Art Of Feature Engineering written by Pablo Duboue and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-25 with Computers categories.
A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.
Explainable Artificial Intelligence An Introduction To Interpretable Machine Learning
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Author : Uday Kamath
language : en
Publisher: Springer Nature
Release Date : 2021-12-15
Explainable Artificial Intelligence An Introduction To Interpretable Machine Learning written by Uday Kamath and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-15 with Computers categories.
This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning. --Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU This is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors! --Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level. Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist. Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group
Explainable Ai For Practitioners
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Author : Michael Munn
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-10-31
Explainable Ai For Practitioners written by Michael Munn and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-31 with Computers categories.
Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow. This essential book provides: A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs Tips and best practices for implementing these techniques A guide to interacting with explainability and how to avoid common pitfalls The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
Feature Engineering For Modern Machine Learning With Scikit Learn
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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-23
Feature Engineering For Modern Machine Learning With Scikit Learn written by Cuantum Technologies LLC and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-23 with Computers categories.
Master feature engineering with Scikit-Learn! Learn to preprocess, transform, and automate data for machine learning. Boost predictive accuracy with pipelines, clustering, and advanced techniques for real-world projects. Key Features Comprehensive guide to feature engineering for Scikit-Learn Hands-on projects for real-world applications Focus on automation, pipelines, and deep learning integration Book DescriptionFeature engineering is essential for building robust predictive models. This book delves into practical techniques for transforming raw data into powerful features using Scikit-Learn. You'll explore automation, deep learning integrations, and advanced topics like feature selection and model evaluation. Learn to handle real-world data challenges, enhance accuracy, and streamline your workflows. Through hands-on projects, readers will gain practical experience with techniques such as clustering, pipelines, and feature selection, applied to domains like retail and healthcare. Step-by-step instructions ensure a comprehensive learning journey, from foundational concepts to advanced automation and hybrid modeling approaches. By combining theory with real-world applications, the book equips data professionals with the tools to unlock the full potential of machine learning models. Whether working with structured datasets or integrating deep learning features, this guide provides actionable insights to tackle any data transformation challenge effectively.What you will learn Create data-driven features for better ML models Apply Scikit-Learn pipelines for automation Use clustering and feature selection effectively Handle imbalanced datasets with advanced techniques Leverage regularization for feature selection Utilize deep learning for feature extraction Who this book is for Data scientists, machine learning engineers, and analytics professionals looking to improve predictive model performance will find this book invaluable. Prior experience with Python and basic machine learning concepts is recommended. Familiarity with Scikit-Learn is helpful but not required.