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.
|License:||GPL (>= 2)|
See the help files for the following functions for more information:
Sergej Potapov, Werner Adler, Benjamin Hofner and Berthold Lausen
Maintainer: Sergej Potapov <firstname.lastname@example.org>
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.