clvarseloutput-class: "clvarseloutput"
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