Diagnostic accuracy of classification models

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Description

Several functions for evaluating the accuracy of classification models. The package provides the following performance measures: repeated k-fold cross-validation, 0.632 and 0.632+ bootstrap estimation of the misclassification rate, sensitivity, specificity and AUC. If an application is computationally intensive, parallel execution can be used to reduce the computational effort.

Details

Package: Daim
Type: Package
Version: 1.1-0
Date: 2013-10-09
License: GPL (>= 2)

See the help files for the following functions for more information:

Daim, performDaim, auc.Daim

Author(s)

Sergej Potapov, Werner Adler, Benjamin Hofner and Berthold Lausen

Maintainer: Sergej Potapov <sergej.potapov@gmail.com>

References

Werner Adler and Berthold Lausen (2009).
Bootstrap Estimated True and False Positive Rates and ROC Curve.
Computational Statistics & Data Analysis, 53, (3), 718–729.

Tom Fawcett (2006).
An introduction to ROC analysis.
Pattern Recognition Letters, 27, (8).

Bradley Efron and Robert Tibshirani (1997).
Improvements on cross-validation: The.632+ bootstrap method.
Journal of the American Statistical Association, 92, (438), 548–560.