Unlike previous algorithms, conditional inference trees rely on statistical significance in the selection of attributes on which to perform partitions. In conditional inference trees, the class attribute is defined as a function of the other attributes (iteratively). In short, the algorithm first searches for the attributes that significantly predict the class, in a null hypothesis test that can be selected in the call of the function. The strongest predictor (if any) is then selected for the first partition. Nodes are created after splitting the partition attribute, if numeric, in a way that maximizes the goodness of the split (we do not detail the required computations here). The algorithm then repeats the operation for each of the nodes and continues until no attribute remains, or none is significantly related to the class. More information is available in the documentation of the partykit
package.
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