As we have seen, there are a number of differences between algorithms. For instance, some use the formula notation and some the matrix notation. The caret package uses a similar notation for all the algorithms it supports. Further, it contains tools that perform sampling operations, such as generating training and testing data with the same characteristics (stratified sampling), the use of boosting of bagging with several algorithms, and cross-validation samples. Examples of cross-validation include, for instance, the use of 10 subsamples, of which one is iteratively used as testing data and the rest as training data (or the leave-one-out cross-validation, where one observation is iteratively used as testing data and the rest as training data). Other features are included as well, such as examining the performance of the classification, as we have done previously. The caret package will be further discussed in Chapter 14, Cross-validation and...
Learning Predictive Analytics with R
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Learning Predictive Analytics with R
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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.
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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