"cloutput"

Description

Object returned by one of the classifiers (functions ending with CMA)

Slots

learnind:

Vector of indices that indicates which observations where used in the learning set.

y:

Actual (true) class labels of predicted observations.

yhat:

Predicted class labels by the classifier.

prob:

A numeric matrix whose rows equals the number of predicted observations (length of y/yhat) and whose columns equal the number of different classes in the learning set. Rows add up to one. Entry j,k of this matrix contains the probability for the j-th predicted observation to belong to class k. Can be a matrix of NAs, if the classifier used does not provide any probabilities

method:

Name of the classifer used.

mode:

character, one of "binary" (if the number of classes in the learning set is two) or multiclass (if it is more than two).

model:

List containing the constructed classifiers.

Methods

show

Use show(cloutput-object) for brief information

ftable

Use ftable(cloutput-object) to obtain a confusion matrix/cross-tabulation of y vs. yhat, s. ftable,cloutput-method.

plot

Use plot(cloutput-object) to generate a probability plot of the matrix prob described above, s. plot,cloutput-method

roc

Use roc(cloutput-object) to compute the empirical ROC curve and the Area Under the Curve (AUC) based on the predicted probabilities, s.roc,cloutput-method

Author(s)

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

See Also

clvarseloutput compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA, scdaCMA, shrinkldaCMA, svmCMA