Description Usage Arguments Value See Also
This is a convenience method implemented over softsplits
and the softening functions from this package.
1 
fit 
A classification tree  either an object 
ds 
A data frame used as training data for softening 
method 
A name of softening method. One of: "DR0", "DR1", "DR2", ..., "ESD", "C4.5", "optim_d", "optim_d^2", "optim_d^4", "optim_auc" The 'method = "DRx"' for some number x: The softening parameters are set according to ‘data ranges’ appropriate to tree nodes. The parameters are configured such that in each node the distance of the boundary of the softened area from split value is 2^{x}r, where r is the distance from the split value to the furthest data point in the tree node projected to the direction from the split value to the boundary. The 'method = "ESD"' sets boundaries of the softening using error standard deviation.
This is how C4.5 method sets "probabilistic splits"; for that reason value The 'method = "optim_d^q"' for some number q: The softening parameters are set
by optimization process which minimizes \code{mean}((1.0p)^q) where p is for each data point in
If 'method = "optim_auc"': The classification tree 
control 
List of additional configuration paramaters. Possible members in the list are:

The ‘soft tree’ structure representing the same tree structure
as given in the parameter fit
,
but with softening parameters set using the given method.
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