classification - R package "tree": what is the difference between minsize and mincut? -

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the tree command constructing classification trees (using "tree" library) configured using tree.control. tree.control page explains minsize , mincut parameters follows:

mincut   minimum number of observations include in either child node. weighted quantity; observational weights used compute ‘number’. default 5.  minsize  smallest allowed node size: weighted quantity. default 10.

to me, these 2 descriptions seem similar things. difference between mincut , minsize?

as understand it, classification, mincut determines minimum number of observations required each class minsize minimum number of observations required node.

for example suppose have 14 observations in node , deciding whether split. if 11 in class , 4 in class b shouldn't split because don't have @ least 5 of each class. if had 10 in class , 5 in class b split.


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