bag.aucoob: AUC on the Out Of Bag samples

In GPLTR: Generalized Partially Linear Tree-Based Regression Model

AUC on the Out Of Bag samples

Description

Compute the AUC on the OOB samples of the bagging procedure for the binomial family. The true and false positive rates are also returned and could be helpfull for plotting the ROC curves.

Usage

bag.aucoob(bag_pltr, xdata, Y.name)

Arguments

bag_pltr	The output of the function bagging.pltr
xdata	The learning dataset containing the dependent variable, the confounding variables and the predictors variables
Y.name	The name of the binary dependent variable

Details

The thresshold values used for computing the AUC are defined when building the bagging predictor. see bagging.pltr for the convenient parameterization.

Value

A list of 4 elements

AUCOOB	the AUC computed on OOB samples of the Bagging procedure
TPR	the true positive rate for several thresshold values
FPR	the false positive rate for several thresshold values
OOB	the Out Of Bag error for each thresshold value

Note

The plot of the ROC curve is straighforward using the TPR and FPR obtained with the function bag.aucoob

Author(s)

Cyprien Mbogning

References

Mbogning, C., Perdry, H., Broet, P.: A Bagged partially linear tree-based regression procedure for prediction and variable selection (submitted 2014)

Examples

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GPLTR documentation built on Aug. 27, 2023, 1:06 a.m.