Through years of consulting, operating their own companies, and working in a number of corporate environments, the authors have developed insights into which methods work and which do not in the "real world." The result of this experience is the development of these fundamental rules for building analysis models to solve complex corporate problems. This book describes some common-sense principles that can be used to ensure that the models and solutions that are developed meet the decision maker's needs. These principles will help you avoid mistakes and help you develop better usable solutions.

Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization. Implementing Analytics simplifies and helps democratize a very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. A technology agnostic methodology that breaks down complex tasks like model design and tuning and emphasizes business decisions rather than the technology behind analytics. Simplifies the understanding of analytics from a technical and functional perspective and shows a wide array of problems that can be tackled using existing technology Provides a detailed step by step approach to identify opportunities, extract requirements, design variables and build and test models. It further explains the business decision strategies to use analytics models and provides an overview for governance and tuning Helps formalize analytics projects from staffing, technology and implementation perspectives Emphasizes machine learning and data mining over statistics and shows how the role of a Data Scientist can be broken down and still deliver the value by building a robust development process

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.

With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner.Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations Demonstrates methods to help sort through data to make better observations and allow you to make better predictions

MULTIPLE CRITERIA DECISION ANALYSIS: State of the Art Surveys is the most comprehensive work available to survey the state of the art in MCDA to date. Its 25 chapters are organized in eight parts and are written by 52 international leading experts. Each of these parts covers one of the central streams of multiple criteria decision analysis literature. These literature streams are: MCDA today, Foundations of MCDA, Our Ranking Methods, Multiattribute Utility Theory, Non-Classical MCDA Approaches, Multiobjective Mathematical Programming, Applications, and MCDM Software. The handbook presents the most up-to-date discussions on well-established methodologies and theories in the field, while systematically surveying emerging fields in MCDA such as conjoint measurement, fuzzy preferences, fuzzy integrals, rough sets, etc. MULTIPLE CRITERIA DECISION ANALYSIS: State of the Art Surveys is a valuable reference volume (more than 2000 references) for the field of decision analysis. It provides graduate students, researchers, and practitioners with a sweeping survey of MCDA theory, methodologies, and applications. It is a handbook that is particularly suitable for use in seminars in Decision Analysis, Decision Support, and Decision Theory.

A proven decision management methodology for increased profits and lowered risks Knowledge Automation: How to Implement Decision Management in Business Processes describes a simple but comprehensive methodology for decision management projects, which use business rules and predictive analytics to optimize and automate small, high-volume business decisions. It includes Decision Requirements Analysis (DRA), a new method for taking the crucial first step in any IT project to implement decision management: defining a set of business decisions and identifying all the information—business knowledge and data—required to make those decisions. Describes all the stages in automating business processes, from business process modeling down to the implementation of decision services Addresses how to use business rules and predictive analytics to optimize and automate small, high-volume business decisions Proposes a simple "top-down" method for defining decision requirements and representing them in a single diagram Shows how clear requirements can allow decision management projects to be run with reduced risk and increased profit Nontechnical and accessible, Knowledge Automation reveals how DRA is destined to become a standard technique in the business analysis and project management toolbox.