Select Parameters for Tree
A utility function for use with the control
argument of tree
.
tree.control(nobs, mincut = 5, minsize = 10, mindev = 0.01)
nobs |
The number of observations in the training set. |
mincut |
The minimum number of observations to include in either child node. This is a weighted quantity; the observational weights are used to compute the ‘number’. The default is 5. |
minsize |
The smallest allowed node size: a weighted quantity. The default is 10. |
mindev |
The within-node deviance must be at least this times that of the root node for the node to be split. |
This function produces default values of mincut
and
minsize
, and ensures that mincut
is at most half
minsize
.
To produce a tree that fits the data perfectly, set mindev = 0
and minsize = 2
, if the limit on tree depth allows such a tree.
A list:
mincut |
The maximum of the input or default |
minsize |
The maximum of the input or default |
nmax |
A estimate of the maximum number of nodes that might be grown. |
nobs |
The input |
The interpretation of mindev
given here is that of Chambers and
Hastie (1992, p. 415), and apparently not what is actually implemented
in S. It seems S uses an absolute bound.
B. D. Ripley
Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.
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