prune.rxDTree: Pruning an rxDTree Decision Tree

Description

Prune a decision tree created by rxDTree and return the smaller tree.

Usage


  prune.rxDTree(tree, cp,   ...  )

Arguments

tree

object returned from a call to rxDTree.

cp

a complexity parameter specifying the complexity at which to prune the tree. Generally, you should examine the cptable component of the tree object to determine a suitable value for cp.

...

additional arguments to be passed to other methods. (There are, in fact, no other methods called by prune.rxDTree.)

Details

The prune.rxDTree function can be used as a prune method for objects of class rxDTree, provided the rpart package is attached prior to attaching RevoScaleR.

Value

an object of class "rxDTree" representing the pruned tree. It is a list with components similar to those of class "rpart" with the following distinctions:

  • where - A vector of integers indicating the node to which each point is allocated. This information is always returned if the data source is a data frame. If the data source is not a data frame and outFile is specified that is, not NULL, the node indices are written/appended to the specified file with a column name as defined by outColName.

For other components, see rpart.object for details.

Author(s)

Microsoft Corporation Microsoft Technical Support

References

Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.

Therneau, T. M. and Atkinson, E. J. (2011) An Introduction to Recursive Partitioning Using the RPART Routines.

Yael Ben-Haim and Elad Tom-Tov (2010) A streaming parallel decision tree algorithm. Journal of Machine Learning Research 11, 849--872.

See Also

rpart, rpart.control, rpart.object.

Examples


 claimsData <- file.path(system.file("SampleData", package="RevoScaleR"), "claims.xdf")
 claimsTree <- rxDTree(type ~ cost + number, data=claimsData, minSplit=20)
 claimsTreePruned <- prune.rxDTree(claimsTree, cp=0.04)
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