DATA MINING
Desktop Survival Guide by Graham Williams |
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ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are useful in illustrating specific pairs of performance measures for classifiers. The ROCR package creates 2D performance curves from any two of over 25 standard performance measures. Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. See demo(ROCR) and http://rocr.bioinf.mpi-sb.mpg.de/ for examples.