- 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
We now need to create a data frame that includes the criterion attribute (quality), the length of the reviews, and the term-document matrix:
DF = as.data.frame(cbind(quality, lengths, SparseRemoved))
Let's now create our training and a testing dataset:
1 set.seed(123) 2 train = sample(1:2000,1000) 3 TrainDF = DF[train,] 4 TestDF = DF[-train,]
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