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A tree is grown by binary recursive partitioning using the response in the specified formula and choosing splits from the terms of the right-hand-side.
TreeModel( mincut = 5, minsize = 10, mindev = 0.01, split = c("deviance", "gini"), k = numeric(), best = integer(), method = c("deviance", "misclass") )
MLModel class object.
MLModel
minimum number of observations to include in either child node.
smallest allowed node size: a weighted quantity.
within-node deviance must be at least this times that of the root node for the node to be split.
splitting criterion to use.
scalar cost-complexity parameter defining a subtree to return.
integer alternative to k requesting the number of terminal nodes of a subtree in the cost-complexity sequence to return.
k
character string denoting the measure of node heterogeneity used to guide cost-complexity pruning.
factor, numeric
factor
numeric
Further model details can be found in the source link below.
tree, prune.tree, fit, resample
tree
prune.tree
fit
resample
# \donttest{ ## Requires prior installation of suggested package tree to run fit(Species ~ ., data = iris, model = TreeModel) # }
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