DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining 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 Glossary Bibliography Index

Basics

Use printcp to view the performance of the model.

> printcp(wine.rpart)

Classification tree:
rpart(formula = Type ~ ., data = wine)

Variables actually used in tree construction:
[1] Dilution   Flavanoids Hue        Proline

Root node error: 107/178 = 0.60112

n= 178

        CP nsplit rel error  xerror     xstd
1 0.495327      0   1.00000 1.00000 0.061056
2 0.317757      1   0.50467 0.47664 0.056376
3 0.056075      2   0.18692 0.28037 0.046676
4 0.028037      3   0.13084 0.23364 0.043323
5 0.010000      4   0.10280 0.21495 0.041825

The predict function will apply the model to data. The data must contain the same variable on which the model was built. If not an error is generated. This is a common problem when wanting to apply the model to a new dataset that does not contain all the same variables, but does contain the variables you are interested in.

> cols <- c("Type", "Dilution", "Flavanoids", "Hue", "Proline")
> predict(wine.rpart, wine[,cols])
Error in eval(expr, envir, enclos) : Object "Alcohol" not found

Fix this up with

> wine.rpart <- rpart(Type ~ Dilution + Flavanoids + Hue + Proline,
data=wine)
> predict(wine.rpart, wine[,cols])
             1          2          3
1   0.96610169 0.03389831 0.00000000
2   0.96610169 0.03389831 0.00000000
[...]
70  0.03076923 0.93846154 0.03076923
71  0.00000000 0.25000000 0.75000000
[...]
177 0.00000000 0.25000000 0.75000000
178 0.00000000 0.02564103 0.97435897

Display a confusion matrix.

> table(predict(wine.rpart, wine, type="class"), wine$Type)

     1  2  3
  1 57  2  0
  2  2 66  4
  3  0  3 44

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