rpartXse: Obtain a tree-based model

In DMwR: Functions and data for "Data Mining with R"

Description

This function is based on the tree-based framework provided by the rpart package (Therneau et. al. 2010). It basically, integrates the tree growth and tree post-pruning in a single function call. The post-pruning phase is essentially the 1-SE rule described in the CART book (Breiman et. al. 1984).

Usage

rpartXse(form, data, se = 1, cp = 0, minsplit = 6, verbose = F, ...)

Arguments

form	A formula describing the prediction problem
data	A data frame containg the training data to be used to obtain the tree-based model
se	A value with the number of standard errors to use in the post-pruning of the tree using the SE rule (defaults to 1)
cp	A value that controls the stopping criteria used to stop the initial tree growth (defaults to 0)
minsplit	A value that controls the stopping criteria used to stop the initial tree growth (defaults to 6)
verbose	The level of verbosity of the function (defaults to F)
...	Any other arguments that are passed to the rpart() function

Details

The x-SE rule for tree post-pruning is based on the cross-validation estimates of the error of the sub-trees of the initially grown tree, together with the standard errors of these estimates. These values are used to select the final tree model. Namely, the selected tree is the smallest tree with estimated error less than the B+x*SE, where B is the lowest estimate of error and SE is the standard error of this B estimate.

Value

A rpart object

Author(s)

Luis Torgo [e-mail]

References

Therneau, T. M. and Atkinson, B.; port by Brian Ripley. (2010). rpart: Recursive Partitioning. R package version 3.1-46.

Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and regression trees. Statistics/Probability Series. Wadsworth & Brooks/Cole Advanced Books & Software.

Torgo, L. (2010) Data Mining using R: learning with case studies, CRC Press (ISBN: 9781439810187).

http://www.dcc.fc.up.pt/~ltorgo/DataMiningWithR

See Also

rt.prune, rpart, prune.rpart

Examples

data(iris)
tree <- rpartXse(Species ~ ., iris)
tree

## A visual representation of the classification tree
## Not run: 
prettyTree(tree)

## End(Not run)

Example output

Loading required package: lattice
Loading required package: grid
n= 150 

node), split, n, loss, yval, (yprob)
      * denotes terminal node

 1) root 150 100 setosa (0.33333333 0.33333333 0.33333333)  
   2) Petal.Length< 2.45 50   0 setosa (1.00000000 0.00000000 0.00000000) *
   3) Petal.Length>=2.45 100  50 versicolor (0.00000000 0.50000000 0.50000000)  
     6) Petal.Width< 1.75 54   5 versicolor (0.00000000 0.90740741 0.09259259)  
      12) Petal.Length< 4.95 48   1 versicolor (0.00000000 0.97916667 0.02083333) *
      13) Petal.Length>=4.95 6   2 virginica (0.00000000 0.33333333 0.66666667) *
     7) Petal.Width>=1.75 46   1 virginica (0.00000000 0.02173913 0.97826087) *

DMwR documentation built on May 1, 2019, 9:17 p.m.