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 Select Data Explore Transform Building Models Two Class Models Unsupervised Modelling Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner Data Graphics in R Understanding Data Preparing Data Building Models Decision Trees 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

Support Vector Machines

Rattle supports the building of support vector machine (SVM) models using the kernlab package for R. This package provides an extensive collection of kernel functions, and a variety of tuning options. The trick with support vector machines is to use the right combination of kernel function and kernel parameters--and this can be quite tricky. Some experimentation with the audit dataset, exploring different kernel functions and parameter settings, identified that a polynomial kernel function with the class weights set to c("0"=4, "1"=1) resulted in the best risk chart. For a 50% caseload we are recovering 94% of the adjustments and 96% of the revenue.

[width=,trim=0 250 0 0, clip]rattle-audit-svm-poly-weights

There are other general parameters that can be set for ksvm, including the cost of constraint violation (the trade-off between the training error and margin? could be from 1 to 1000, and is 1 by default).

For best results it is often a good idea to scale the numeric variables to have a mean of 0 and a standard deviation of 1.

For polynominal kernels you can choose the degree of the polynomial. The default is 1. For the audit data as you increase the degree the model gets much more accurate on the training data but the model generalises less well, as exhibited by its performance on the test dataset.

Another parameter often needing setting for a radial basis function kernel is the sigma value. Rattle uses automatic sigma estimation (sigest) for this kernel, to find the best sigma, and so the user need not set a sigma value. If we wanted to experiment with various sigma values we can copy the R code from the []Log tab and paste it into the R console, add in the additional settings, and run the model. Assuming the results are assigned into the variable crs$ksvm, as in the Log, we can the evaluate the perfromance of this new model using the Evaluate tab.

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