clvarseloutput-class: "clvarseloutput"

Description Slots Extends Methods Author(s) See Also

Description

Object returned by all classifiers that can peform variable selection or compute variable importance. These are:

. Objects of class clvarseloutput extend both the class cloutuput and varsel, s. below.

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).

varsel:

numeric vector of variable importance measures (for Random Forest) or absolute values of regression coefficients (for the other three methods mentionned above) (from which the majority will be zero).

Extends

Class "cloutput", directly. Class "varseloutput", directly.

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 [email protected]

Anne-Laure Boulesteix [email protected]

See Also

rfCMA, compBoostCMA, LassoCMA, ElasticNetCMA


chbernau/CMA documentation built on May 17, 2019, 12:04 p.m.