- Learning Predictive Analytics with R
- Table of Contents
- Learning Predictive Analytics with R
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Setting GNU R for Predictive Analytics
- 2. Visualizing and Manipulating Data Using R
- 3. Data Visualization with Lattice
- 4. Cluster Analysis
- 5. Agglomerative Clustering Using hclust()
- 6. Dimensionality Reduction with Principal Component Analysis
- 7. Exploring Association Rules with Apriori
- 8. Probability Distributions, Covariance, and Correlation
- 9. Linear Regression
- 10. Classification with k-Nearest Neighbors and Naïve Bayes
- 11. Classification Trees
- 12. Multilevel Analyses
- 13. Text Analytics with R
- 14. Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
- A. Exercises and Solutions
- Exercises
- Chapter 1 – Setting GNU R for Predictive Modeling
- Chapter 2 – Visualizing and Manipulating Data Using R
- Chapter 3 – Data Visualization with Lattice
- Chapter 4 – Cluster Analysis
- Chapter 5 – Agglomerative Clustering Using hclust()
- Chapter 6 – Dimensionality Reduction with Principal Component Analysis
- Chapter 7 – Exploring Association Rules with Apriori
- Chapter 8 – Probability Distributions, Covariance, and Correlation
- Chapter 9 – Linear Regression
- Chapter 10 – Classification with k-Nearest Neighbors and Naïve Bayes
- Chapter 11 – Classification Trees
- Chapter 12 – Multilevel Analyses
- Chapter 13 – Text Analytics with R
- Solutions
- Chapter 1 – Setting GNU R for Predictive Modeling
- Chapter 2 – Visualizing and Manipulating Data Using R
- Chapter 3 – Data Visualization with Lattice
- Chapter 4 – Cluster Analysis
- Chapter 5 – Agglomerative Clustering Using hclust()
- Chapter 6 – Dimensionality Reduction with Principal Component Analysis
- Chapter 7 – Exploring Association Rules with Apriori
- Chapter 8 – Probability Distributions, Covariance, and Correlation
- Chapter 9 – Linear Regression
- Chapter 10 – Classification with k-Nearest Neighbors and Naïve Bayes
- Chapter 11 – Classification Trees
- Chapter 12 – Multilevel Analyses
- Chapter 13 – Text Analytics with R
- Exercises
- B. Further Reading and References
- Preface
- Chapter 1 – Setting GNU R for Predictive Modeling
- Chapter 2 – Visualizing and Manipulating Data Using R
- Chapter 3 – Data Visualization with Lattice
- Chapter 4 – Cluster Analysis
- Chapter 5 – Agglomerative Clustering Using hclust()
- Chapter 6 – Dimensionality Reduction with Principal Component Analysis
- Chapter 7 – Exploring Association Rules with Apriori
- Chapter 8 – Probability Distributions, Covariance, and Correlation
- Chapter 9 – Linear Regression
- Chapter 10 – Classification with k-Nearest Neighbors and Naïve Bayes
- Chapter 11 – Classification Trees
- Chapter 12 – Multilevel Analyses
- Chapter 13 – Text Analytics with R
- Chapter 14 – Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
- Index
In this chapter, we discussed the basics of cross-validation and bootstrapping as well as exporting predictive models using PMML. We saw some examples of how to perform cross-validation and bootstrapping using the caret package. We showed how to predict data usage from the models we created. We explored the PMML structure, examined how to export to PMML, and saw how to save the resulting object to an XML file for use in other software packages.
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