predict.imptree: Classification with Imprecise Probabilities

In imptree: Classification Trees with Imprecise Probabilities

Description

Prediction of imptree objects

Usage

## S3 method for class 'imptree'
predict(object, data, dominance = c("strong", "max"),
  utility = 0.65, ...)

## S3 method for class 'evaluation_imptree'
print(x, ...)

Arguments

object	An object of class imptree. See details.
data	Data.frame containing observations to be predicted. If NULL the observations in the training set of "object" are employed.
dominance	Dominace criterion to be applied when predicting classes. This may either be "strong" (default) or "max". See details.
utility	Utility for the utility based accuracy measure for a vacuous prediction result (default: 0.65).
...	Additional arguments for data. May be "weights", "subset", "na.action", any further are discarded.
x	an object of class evaluation_imptree

Details

This function carries out the prediction of an imprecise tree. An existence check on the stored C++ object reference is carried out at first. If the reference is not valid the original call for "object" is printed as error.

There are currently 2 different dominance criteria available:

max

Maximum frequency criterion. Dominance is decided only by the upper bound of the probability interval, ie. a state C_i is dominated if there exists any j != i with u(C_i) < u(C_j)

strong

Interval dominance criterion. For the IDM it coincides with the strong dominance criterion. Here a state C_i is dominated if there exists any j != i with u(C_i) < l(C_j)

Value

predict.imptree() return an object of class evaluation_imptree, which is a named list containing predicted classes, predicted probability distribution and accuracy evaluation

probintlist	List of the imprecise probability distributions of the class variable. One matrix per observation in the test data.
classes	Predicted class(es) of the observations as boolean matrix
evaluation	Result of accuracy evaluation nObs: Number of observations deter: Determinacy nObsIndet: Number of observations with indeterminate prediction indetSize: Average number of classes when predicting indeterminate (NA when no indeterminate observation) acc_single: Single-set accuracy (NA when no determinate observation) acc_set: Set-accuracy (NA when no indeterminate observation) acc_disc: Discounted-accuracy acc_util: Utility based (discounted) accuracy

The printing function returns the evaluation_imptree object invisibly.

Author(s)

Paul Fink [e-mail]

See Also

imptree, node_imptree

Examples

data("carEvaluation")

## create a tree with IDM (s=1) to full size
## carEvaluation, leaving the first 10 observations out
ip <- imptree(acceptance~., data = carEvaluation[-(1:10),], 
  method="IDM", method.param = list(splitmetric = "globalmax", s = 1), 
  control = list(depth = NULL, minbucket = 1))

## predict the first 10 observations with 'max' dominance
pp <- predict(ip, dominance = "max", data = carEvaluation[(1:10),])
print(pp)
pp$classes                ## predicted classes as logical matrix

## predict the first 10 observations with 'strong' dominance and
## use a different level of utility
predict(ip, dominance = "strong", data = carEvaluation[(1:10),],
        utility = 0.5)

imptree documentation built on May 1, 2019, 8:18 p.m.