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Receiver\noperating characteristic (ROC) curve is a common tool for\nassessing overall diagnostic ability of the binary classifier.\nUnlike depending on a certain threshold, area under ROC curve\n(also known as AUC), is a summary statistic about how well a\nbinary classifier performs overall for the classification task.\nROCit package provides flexibility to easily evaluate\nthreshold-bound metrics. Also, ROC curve, along with AUC, can\nbe obtained using different methods, such as empirical,\nbinormal and non-parametric. ROCit encompasses a wide variety\nof methods for constructing confidence interval of ROC curve\nand AUC. ROCit also features the option of constructing\nempirical gains table, which is a handy tool for direct\nmarketing. The package offers options for commonly used\nvisualization, such as, ROC curve, KS plot, lift plot. Along\nwith in-built default graphics setting, there are rooms for\nmanual tweak by providing the necessary values as function\narguments. 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