Description Usage Arguments Value Author(s) References See Also Examples
imptree
implements Abellan and Moral's tree
algorithm (based on Quinlans ID3) for classification. It
employes either the imprecise Dirichlet model (IDM) or
nonparametric predictive inference (NPI) to generate the
imprecise probability distribution of the classification variable
within a node.
1 2 3 4 5 6 7 8 |
formula |
Formula describing the strucutre (class variable ~ featutre variables). Any interaction terms trigger an error. |
data |
Data.frame to evaluate supplied formula on. If not provided the the formula is evaluated on the calling environment |
weights |
Individual weight of the observations (default: 1 to each). This argument is ignored at the moment. |
control |
A named (partial) list according to the result of
|
method |
Method applied for calculating the probability
intervals of the class probability. |
method.param |
Named list providing the method specific
parameters. See |
... |
optional parameters to be passed to the main function
|
x |
A data.frame or a matrix of feature variables. The columns are required to be named. |
y |
The classification variable as a factor. |
An object of class imptree
, which is a list
with the following components:
call |
Original call to |
tree |
Object reference to the underlying C++ tree object. |
train |
Training data in the form required by the
workhorse C++ function. |
formula |
The formula describing the data structure |
Paul Fink Paul.Fink@stat.uni-muenchen.de, based on algorithms by J. Abellán and S. Moral for the IDM and R. M. Baker for the NPI approach.
Abellán, J. and Moral, S. (2005), Upper entropy of credal sets. Applications to credal classification, International Journal of Approximate Reasoning 39, 235–255.
Strobl, C. (2005), Variable Selection in Classification Trees Based on Imprecise Probabilities, ISIPTA'05: Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications, 339–348.
Baker, R. M. (2010), Multinomial Nonparametric Predictive Inference: Selection, Classification and Subcategory Data.
predict.imptree
for prediction,
summary.imptree
for summary information,
imptree_params
and imptree_control
for
arguments controlling the creation, node_imptree
for
accessing a specific node in the tree
1 2 3 4 5 6 7 8 9 10 11 12 | data("carEvaluation")
## create a tree with IDM (s=1) to full size on
## carEvaluation, leaving the first 10 observations out
imptree(acceptance~., data = carEvaluation[-(1:10),],
method="IDM", method.param = list(splitmetric = "globalmax", s = 1),
control = list(depth = NULL, minbucket = 1)) # control args as list
## same setting as above, now passing control args in '...'
imptree(acceptance~., data = carEvaluation[-(1:10),],
method="IDM", method.param = list(splitmetric = "globalmax", s = 1),
depth = NULL, minbucket = 1)
|
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