In this case, the classification scores need to be provided separatelyįor the data points of the negative/background class. Weighted, hard-labeled classification problems:Įach data point is uniquely assigned to one out of two possible classes, where each data points additionally has a weight assigned, for instance In this case, the classification scores may be either provided separatelyįor the data points of each of the classes, i.e., as scores.class0 for the data points from the positive/foreground class and as scores.class1įor the data points of the negative/background class or the classification scores for all data points are provided as scores.class0 and the labelsĪre provided as numerical values ( 1 for the positive class, 0 for the negative class) as weights.class0.Ģ. Standard, hard-labeled classification problems:Įach data point is uniquely assigned to one out of two possible classes. It can be used under different scenarios:ġ. This function computes the area under a receiver-operating characteristic (ROC) curve and, optionally, the curve itself and returns it as a PRROC object (see below). TRUE if the ROC curve of a random classifier given the supplied weights should be computed TRUE if the minimum ROC curve given the supplied weights should be computed TRUE if the maximum ROC curve given the supplied weights should be computed TRUE if the curve should also be returned, FALSE otherwise The weights for the data points of the negative class in same ordering as scores.class1 (optional) The weights for the data points of the positive class in same ordering as scores.class0 (optional) The scores of the negative class if provided separately (see scores.class0) Need to be provided only for soft-labelling and should be of the same length as scores.class0 and scores.class1, respectively. In this case, weights.class0 and weights.class1 In the second case, the scores for the negative data points need to be provided in scores.class1. The soft-labels for the positive class, i.e., the probability for each data point to belong to the positive class.Īccordingly, weights.class1 should be left at the default value (1-weights.class0). In addition, weights.class0 needs to contain the class labels of the data points (1 for positive class, 0 for negative class) or In the first case, scores.class1 should not be assigned an explicit value, but left at the default (scores.class1=scores.class0). The classification scores of i) all data points or ii) only the data points belonging to the positive class. Roc.curve( scores.class0, scores.class1=scores.class0, weights.class0=NULL, In addition to the area under the curve, the curve can be obtained by setting argument curve to TRUE. Ĭomputes the area under the receiver operating characteristics (ROC) curve for weighted and unweighted data.

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