Recursive Partitioning and Regression Tree Models
Fit an rpart model.
RPartModel( minsplit = 20, minbucket = round(minsplit/3), cp = 0.01, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, xval = 10, surrogatestyle = 0, maxdepth = 30 )
minsplit |
minimum number of observations that must exist in a node in order for a split to be attempted. |
minbucket |
minimum number of observations in any terminal node. |
cp |
complexity parameter. |
maxcompete |
number of competitor splits retained in the output. |
maxsurrogate |
number of surrogate splits retained in the output. |
usesurrogate |
how to use surrogates in the splitting process. |
xval |
number of cross-validations. |
surrogatestyle |
controls the selection of a best surrogate. |
maxdepth |
maximum depth of any node of the final tree, with the root node counted as depth 0. |
factor, numeric, Surv
cp
Further model details can be found in the source link below.
MLModel class object.
## Requires prior installation of suggested packages rpart and partykit to run fit(Species ~ ., data = iris, model = RPartModel)
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