Just Enough R!: An Interactive Approach to Machine Learning and Analytics

Just Enough R! : An Interactive Approach to Machine Learning and Analytics book cover
Just Enough R!: An Interactive Approach to Machine Learning and Analytics
By 作者:Richard J. Roiger
Pages 页数: 364 pages
Publisher Finelybook 出版社: Chapman and Hall/CRC; 1 edition (15 Jun. 2020)
Language 语言: English
ISBN-10 书号:0367443201
ISBN-13 书号:9780367443207
Book Description to Finelybook sorting
Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called “seeing then doing” as it first gives step-By 作者:-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed By 作者:detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations given in the text.
Features

Gets you quickly using R as a problem-solving tool
Uses RStudio’s integrated development environment
Shows how to interface R with SQLite
Includes examples using R’s Rattle graphical user interface
Requires no prior knowledge of R, machine learning, or computer programming
Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again
Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression
Includes end-of-chapter exercises, many of which can be solved By 作者:modifying existing scripts
Includes datasets from several areas, including business, health and medicine, and science

Contents
Preface
Acknowledgment
Author
Chapter 1 lintroduction to Machine Learning
Chapter 2 Introduction to R
Chapter 3 Data Structures and Manipulation
Chapter 4 Preparing the Data
Chapter 5 Supervised Statistical Techniques
Chapter 6 Tree-Based Methods
Chapter 7 Rule-Based Techniques
Chapter 8 Neural Networks
Chapter 9 Formal Evaluation Techniques
Chapter 10 Support Vector Machines
Chapter 11 Unsupervised Clustering Techniques
Chapter 12 A Case Study in Predicting Treatment Outcome
Bibliography
Appendix A:Supplementary Materials and More Datasets
Appendix B:Statistics for Performance Evaluation
Subject Index
Index of R Functions
Script Index


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