if TRUE, then bootstrap samples are drawn from the
training set using the observation weights at each iteration. If
FALSE, then all observations are used with their weights.
mfinal
number of iterations for which boosting is run.
coeflearn
learning algorithm.
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.
Details
Response types:
factor
Automatic tuning of grid parameters:
mfinal, maxdepth, coeflearn*
* excluded from grids by default
Further model details can be found in the source link below.