Just Enough R! An Interactive Approach to Machine Learning and Analytics
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-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by 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.
- 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 modifying existing scripts
- Includes datasets from several areas, including business, health and medicine, and science
About the Author
Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.
Preface. Acknowledgment. Author. Introduction to Machine Learning. Introduction to R. Data Structures and Manipulation. Preparing the Data. Supervised Statistical Techniques. Tree-Based Methods. Rule-Based Techniques. Neural Networks. Formal Evaluation Techniques. Support Vector Machines. Unsupervised Clustering Techniques. 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.