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

Basics

Use 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 functionsprintcp 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 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 functionspredict 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

cXnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)cR functionsR functions predictXnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)predictR functionsR functions

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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