Decision Trees for Decision Making
Author | : John F. Magee |
Publisher | : |
Total Pages | : 13 |
Release | : 1964 |
Genre | : Business logistics |
ISBN | : |
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Author | : John F. Magee |
Publisher | : |
Total Pages | : 13 |
Release | : 1964 |
Genre | : Business logistics |
ISBN | : |
Author | : Helen C. Abell Collection |
Publisher | : |
Total Pages | : 26 |
Release | : 1972 |
Genre | : |
ISBN | : |
Author | : Magee |
Publisher | : |
Total Pages | : |
Release | : 1964-01-01 |
Genre | : |
ISBN | : 9780000644107 |
Author | : Christoph Molnar |
Publisher | : Lulu.com |
Total Pages | : 320 |
Release | : 2020 |
Genre | : Artificial intelligence |
ISBN | : 0244768528 |
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.
Author | : Chiheb Chebbi |
Publisher | : Packt Publishing Ltd |
Total Pages | : 264 |
Release | : 2018-06-27 |
Genre | : Language Arts & Disciplines |
ISBN | : 178899311X |
Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
Author | : Mark Koning |
Publisher | : Independently Published |
Total Pages | : 168 |
Release | : 2017-10-04 |
Genre | : Computers |
ISBN | : 9781549893759 |
If you want to learn how decision trees and random forests work, plus create your own, this visual book is for you. The fact is, decision tree and random forest algorithms are powerful and likely touch your life everyday. From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services. They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own algorithms in Python, this book is for you.
Author | : Oleksandr Sosnovshchenko |
Publisher | : Packt Publishing Ltd |
Total Pages | : 371 |
Release | : 2018-02-28 |
Genre | : Computers |
ISBN | : 1787123529 |
Leverage the power of machine learning and Swift programming to build intelligent iOS applications with ease Key Features Implement effective machine learning solutions for your iOS applications Use Swift and Core ML to build and deploy popular machine learning models Develop neural networks for natural language processing and computer vision Book Description Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves. What you will learn Learn rapid model prototyping with Python and Swift Deploy pre-trained models to iOS using Core ML Find hidden patterns in the data using unsupervised learning Get a deeper understanding of the clustering techniques Learn modern compact architectures of neural networks for iOS devices Train neural networks for image processing and natural language processing Who this book is for iOS developers who wish to create smarter iOS applications using the power of machine learning will find this book to be useful. This book will also benefit data science professionals who are interested in performing machine learning on mobile devices. Familiarity with Swift programming is all you need to get started with this book.
Author | : Wayne W. Daniel |
Publisher | : |
Total Pages | : 20 |
Release | : 1979 |
Genre | : Decision making |
ISBN | : |
Author | : Christina H. Gladwin |
Publisher | : SAGE |
Total Pages | : 112 |
Release | : 1989-09 |
Genre | : Social Science |
ISBN | : 9780803934870 |
Why do people in a certain group behave the way they do? And, more importantly, what specific criteria was used by the group in question? This book presents a method for answering these questions.
Author | : Dennis V. Lindley |
Publisher | : |
Total Pages | : 232 |
Release | : 1985-12-23 |
Genre | : Business & Economics |
ISBN | : |
Making Decisions Second Edition D.V. Lindley Formerly Professor of Statistics, University College London This book looks at the problems involved in decision-making and argues that there is only one logical way to make a decision. By the use of three basic principles--assigning probabilities to the uncertain events; assigning utilities to the possible consequences; and choosing that decision that maximizes expected utility--decisions can be reached more efficiently and with less disagreement. It shows that only maximization of expected utility leads to sensible decision-making. This extensively revised second edition uses only elementary mathematics and will be of interest to all those concerned with decision-making and its consequences. Since his retirement from University College London in 1977 Professor Lindley has held visiting appointments at Berkeley, University of Florida, George Washington University, University of Sao Paulo, University of Wisconsin, Monash University, Australia, and University of Canterbury, New Zealand. Contents * Decisions and uncertain events * A numerical measure for uncertainty * The laws of probability * A numerical measure for consequences * The utility of money * Bayes' Theorem * Value of information * Decision trees * The assessment of probabilities and utilities * An appreciation Appendix Answers to exercises Glossary of Symbols Subject Index