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BL
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| Record Number
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851416
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| Main Entry
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Burger, Scott, V.
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| Title & Author
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Introduction to machine learning with R : : rigorous mathematical analysis /\ Scott V. Burger.
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| Edition Statement
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First edition.
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| Publication Statement
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Sebastopol, CA :: O'Reilly Media, Inc.,, 2018.
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| Page. NO
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1 online resource (200 pages)
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| ISBN
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149197639X
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: 1491976411
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: 1491976438
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: 1491976446
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: 9781491976395
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: 9781491976418
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: 9781491976432
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: 9781491976449
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9781491976449
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| Contents
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Cover; Copyright; Table of Contents; Preface; Who Should Read This Book?; Scope of the Book; Conventions Used in This Book; O'Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. What Is a Model?; Algorithms Versus Models: What's the Difference?; A Note on Terminology; Modeling Limitations; Statistics and Computation in Modeling; Data Training; Cross-Validation; Why Use R?; The Good; R and Machine Learning; The Bad; Summary; Chapter 2. Supervised and Unsupervised Machine Learning; Supervised Models; Regression; Training and Testing of Data; Classification; Logistic Regression
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Naive Bayes ClassificationBayesian Statistics in a Nutshell; Application of Naive Bayes; Principal Component Analysis; Linear Discriminant Analysis; Support Vector Machines; k-Nearest Neighbors; Regression Using kNN; Classification Using kNN; Summary; Chapter 8. Machine Learning with the caret Package; The Titanic Dataset; Data Wrangling; caret Unleashed; Imputation; Data Splitting; caret Under the Hood; Model Training; Comparing Multiple caret Models; Summary; Appendix A. Encyclopedia of Machine Learning Models in caret; Index; About the Author; Colophon
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Neural Networks for ClassificationNeural Networks with caret; Regression; Classification; Summary; Chapter 6. Tree-Based Methods; A Simple Tree Model; Deciding How to Split Trees; Tree Entropy and Information Gain; Pros and Cons of Decision Trees; Tree Overfitting; Pruning Trees; Decision Trees for Regression; Decision Trees for Classification; Conditional Inference Trees; Conditional Inference Tree Regression; Conditional Inference Tree Classification; Random Forests; Random Forest Regression; Random Forest Classification; Summary; Chapter 7. Other Advanced Methods
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Polynomial RegressionGoodness of Fit with Data--The Perils of Overfitting; Root-Mean-Square Error; Model Simplicity and Goodness of Fit; Logistic Regression; The Motivation for Classification; The Decision Boundary; The Sigmoid Function; Binary Classification; Multiclass Classification; Logistic Regression with Caret; Summary; Linear Regression; Logistic Regression; Chapter 5. Neural Networks in a Nutshell; Single-Layer Neural Networks; Building a Simple Neural Network by Using R; Multiple Compute Outputs; Hidden Compute Nodes; Multilayer Neural Networks; Neural Networks for Regression
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Supervised Clustering MethodsMixed Methods; Tree-Based Models; Random Forests; Neural Networks; Support Vector Machines; Unsupervised Learning; Unsupervised Clustering Methods; Summary; Chapter 3. Sampling Statistics and Model Training in R; Bias; Sampling in R; Training and Testing; Roles of Training and Test Sets; Why Make a Test Set?; Training and Test Sets: Regression Modeling; Training and Test Sets: Classification Modeling; Cross-Validation; k-Fold Cross-Validation; Summary; Chapter 4. Regression in a Nutshell; Linear Regression; Multivariate Regression; Regularization
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| Abstract
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Machine learning can be a difficult subject if you're not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the statistical programming language R. You'll start with the basics like regression, then move into more advanced topics like neural networks, and finally delve into the frontier of machine learning in the R world with packages like Caret. By developing a familiarity with topics like understanding the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Knowing when to use a specific model or not can mean the difference between a highly accurate model and a completely useless one. This book provides copious examples to build a working knowledge of machine learning. Understand the major parts of machine learning algorithms Recognize how machine learning can be used to solve a problem in a simple manner Figure out when to use certain machine learning algorithms versus others Learn how to operationalize algorithms with cutting edge packages
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| Subject
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R (Computer program language)
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| Subject
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Statistics-- Data processing.
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COMPUTERS-- Programming Languages-- General.
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R (Computer program language)
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| Subject
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Statistics-- Data processing.
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| Dewey Classification
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005.13/3
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| LC Classification
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QA76.73.R3B87 2018
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