- 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
C5.0 is an improvement of C4.5. New features include boosting and winnowing. The aim of boosting is to increase the reliability of the predictions by performing the analysis iteratively and adjusting observation weights after each iteration. Higher weight is given to misclassified observations giving them a higher importance in the classification, which usually makes the predictions better. Winnowing refers to the suppression of useless attributes for the main analysis.
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