MKclass (version 0.5)

decisionStump: Compute Decision Stumps

Description

The function computes a decision stump for binary classification also known as 1-level decision tree or 1-rule.

Usage

decisionStump(pred, truth, namePos, perfMeasure = "YJS",
          MAX = TRUE, parallel = FALSE, ncores, delta = 0.01, ...)

Value

Object of class decisionStump.

Arguments

pred

numeric values that shall be used for classification; e.g. probabilities to belong to the positive group.

truth

true grouping vector or factor.

namePos

value representing the positive group; i.e., the name of the category where one expects higher values for pred.

perfMeasure

a single performance measure computed by function perfMeasure.

MAX

logical value. Whether to maximize or minimize the performacne measure.

parallel

logical value. If TRUE packages foreach and doParallel are used to parallelize the computations.

ncores

integer value, number of cores that shall be used to parallelize the computations.

delta

numeric value for setting up grid for optimization; start is minimum of pred-delta, end is maximum of pred+delta.

...

further arguments passed to function prefMeasures.

Author

Matthias Kohl

Details

The function is able to compute a decision stump for various performance measures, all performance measures that are implemented in function perfMeasures. Of course, for several of them the computation is not really usefull such as sensitivity or specificity where one will get trivial decision rules.

In addition, a decision stump will only give a meaningful result if there is a monotone relationship between the two categories and the numeric values given in pred. In such a case the name of the category where one expects higher values should be given in namePos.

References

W. Iba and P. Langley (1992). Induction of One-Level Decision Trees. In: Machine Learning Proceedings 1992, pages 233-240. URL: https://doi.org/10.1016/B978-1-55860-247-2.50035-8

R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. In: Machine Learning, pages 63-91. URL: https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.2711

Examples

## example from dataset infert
fit <- glm(case ~ spontaneous+induced, data = infert, family = binomial())
pred <- predict(fit, type = "response")
res <- decisionStump(pred, truth = infert$case, namePos = 1)
predict(res, newdata = seq(from = 0, to = 1, by = 0.1))