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The Decision Tree

2011-03-01 Medical
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Release : 2011-03-01
Genre : Medical
Kind : eBook
Book Rating : 684/5 ( reviews)

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Book Synopsis The Decision Tree by : Thomas Goetz

Download or read book The Decision Tree written by Thomas Goetz. This book was released on 2011-03-01. Available in PDF, EPUB and Kindle. Book excerpt: For all the talk about personalized medicine, our health care system remains a top-down, doctor-driven system where individuals are too often bit players in their own health decisions. In The Decision Tree, Thomas Goetz proposes a new strategy for thinking about health, one that applies cutting-edge technology to put us at the center of the equation and explains how the new frontier of health care can impact each of our lives.

Decision Trees for Decision Making

1964 Business logistics
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Release : 1964
Genre : Business logistics
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Decision Trees for Decision Making by : John F. Magee

Download or read book Decision Trees for Decision Making written by John F. Magee. This book was released on 1964. Available in PDF, EPUB and Kindle. Book excerpt:

Interpretable Machine Learning

2020 Artificial intelligence
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Release : 2020
Genre : Artificial intelligence
Kind : eBook
Book Rating : 528/5 ( reviews)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar. This book was released on 2020. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Decision Trees and Random Forests

2017-10-04 Computers
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Release : 2017-10-04
Genre : Computers
Kind : eBook
Book Rating : 759/5 ( reviews)

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Book Synopsis Decision Trees and Random Forests by : Mark Koning

Download or read book Decision Trees and Random Forests written by Mark Koning. This book was released on 2017-10-04. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Automatic Design of Decision-Tree Induction Algorithms

2015-02-04 Computers
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Release : 2015-02-04
Genre : Computers
Kind : eBook
Book Rating : 313/5 ( reviews)

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Book Synopsis Automatic Design of Decision-Tree Induction Algorithms by : Rodrigo C. Barros

Download or read book Automatic Design of Decision-Tree Induction Algorithms written by Rodrigo C. Barros. This book was released on 2015-02-04. Available in PDF, EPUB and Kindle. Book excerpt: Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.

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