DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Installing GTK, R, and Rattle Glossary Bibliography Index

Min Split (minsplit)

The minsplit specifies the minimum number of observations that must exist at a node in the tree before any further splitting will be attempted.

Using rpart directly we specify minsplit within an option called control which takes the results from a function called XnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)R functionsR functionsrpart.control. In this example we

> audit <- read.csv(url("http://rattle.togaware.com/audit.csv"))
> audit.rpart <- rpart(TARGET_Adjusted ~ Age + Marital 
                                             + Occupation 
                                             + Deductions, 
                       data=audit,
                       method="class", 
                       control=rpart.control(minsplit=150))
> audit.rpart

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