In this chapter, we have seen how k-NN and Naïve Bayes work by programming our own implementation of the algorithms. You have discovered how to perform these analyses in R. We have shown you that it is not optimal to test our classifier with the data it has been trained with. We have seen that the number of neighbors selected in k-NN impacts the performance of the classification and examined different performance measures. In the next chapter, you will learn about decision trees.
Learning Predictive Analytics with R
By :
Learning Predictive Analytics with R
By:
Overview of this book
This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data.
You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further.
The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages.
Table of Contents (23 chapters)
Learning Predictive Analytics with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Setting GNU R for Predictive Analytics
Visualizing and Manipulating Data Using R
Data Visualization with Lattice
Cluster Analysis
Agglomerative Clustering Using hclust()
Dimensionality Reduction with Principal Component Analysis
Exploring Association Rules with Apriori
Probability Distributions, Covariance, and Correlation
Linear Regression
Classification with k-Nearest Neighbors and Naïve Bayes
Classification Trees
Multilevel Analyses
Text Analytics with R
Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
Exercises and Solutions
Further Reading and References
Index
Customer Reviews