{ "_id": "6a4a1204b9bf02698bd57c07", "Package": "ROCit", "Language": "en-US", "Type": "Package", "Title": "Performance Assessment of Binary Classifier with Visualization", "Version": "2.1.1", "Date": "2020-06-14", "Authors@R": "c(person(\"Md Riaz Ahmed\", \"Khan\", email = \"mdriazahmed.khan@jacks.sdstate.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Thomas\", \"Brandenburger\", email = \"thomas.brandenburger@sdstate.edu\", role = \"aut\" ))", "Description": "Sensitivity (or recall or true positive rate), false\npositive rate, specificity, precision (or positive predictive\nvalue), negative predictive value, misclassification rate,\naccuracy, F-score- these are popular metrics for assessing\nperformance of binary classifier for certain threshold. These\nmetrics are calculated at certain threshold values. 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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