Desktop Survival Guide
by Graham Williams

Tutorial Example

The ensemble approaches build upon the decision tree model builder by building many decision tress through sampling the training dataset in various ways. The ada boosting algorithm is deployed by Rattle to provide its boosting model builder. With the default settings a very reasonable model can be built. At a 60% caseload we are recovering 98% of the cases that required adjustment and 98% of the revenue.

Note in printing a tree how n=xxxx and xxxx is 50% of the total number of training instances. This is because bag.frac=0.5. Maybe need an option in the interface to control this?

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