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Introduction to machine learning with R : rigorous mathematical analysis /
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 in...
| Call Number: | Libro Electrónico |
|---|---|
| Main Author: | |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
Sebastopol, CA :
O'Reilly Media, Inc.,
2018.
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| Edition: | First edition. |
| Subjects: | |
| Online Access: | Texto completo (Requiere registro previo con correo institucional) |
Table of Contents:
- 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
- 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
- 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
- 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
- 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