confmat.vcr {classmap} | R Documentation |
Build a confusion matrix from the output of a function vcr.*.*
.
Description
Build a confusion matrix from the output of a function vcr.*.*
.
Optionally, a separate column for outliers can be added to the confusion matrix.
Usage
confmat.vcr(vcrout, cutoff = 0.99, showClassNumbers = FALSE,
showOutliers = TRUE, silent = FALSE)
Arguments
vcrout |
output of |
cutoff |
cases with overall farness |
showClassNumbers |
if |
showOutliers |
if |
silent |
if |
Value
A confusion matrix
Author(s)
Raymaekers J., Rousseeuw P.J.
References
Raymaekers J., Rousseeuw P.J., Hubert M. (2021). Class maps for visualizing classification results. Technometrics, appeared online. doi: 10.1080/00401706.2021.1927849(link to open access pdf)
See Also
vcr.da.train
, vcr.da.newdata
,
vcr.knn.train
, vcr.knn.newdata
,
vcr.svm.train
, vcr.svm.newdata
,
vcr.rpart.train
, vcr.rpart.newdata
,
vcr.forest.train
, vcr.forest.newdata
,
vcr.neural.train
, vcr.neural.newdata
Examples
vcrout <- vcr.knn.train(scale(iris[, 1:4]), iris[, 5], k = 5)
# The usual confusion matrix:
confmat.vcr(vcrout, showOutliers = FALSE)
# Cases with ofarness > cutoff are flagged as outliers:
confmat.vcr(vcrout, cutoff = 0.98)
# With the default cutoff = 0.99 only one case is flagged here:
confmat.vcr(vcrout)
# Note that the accuracy is computed before any cases
# are flagged, so it does not depend on the cutoff.
confmat.vcr(vcrout, showClassNumbers = TRUE)
# Shows class numbers instead of labels. This option can
# be useful for long level names.
# For more examples, we refer to the vignettes:
## Not run:
vignette("Discriminant_analysis_examples")
vignette("K_nearest_neighbors_examples")
vignette("Support_vector_machine_examples")
vignette("Rpart_examples")
vignette("Random_forest_examples")
vignette("Neural_net_examples")
## End(Not run)