{'fraction_non_alphanumeric': 0.043645699614890884, 'fraction_numerical': 0.008985879332477536, 'mean_word_length': 5.0, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Various performance measures in Binary classification –An Overview of ROC study

The Receiver Operating Characteristic (ROC) curve is used to test the accuracy of a diagnostic test especially in medical related fields. Construction of an ROC curve is based on four possible counts or states. The ROC curve is a plot of Sensitivity vs (1-Specificity) and is drawn between the coordinates (0,0) and (1,1). The accuracy of an diagnostic test can be measured by the Area Under the ROC Curve (AUC). The AUC is always lies between (0, 1). Other performance measures like Positive likelihood, Negative likelihood, Precision, Recall, Youden’s Index, Matthews correlation, Discriminant Power (DP) etc., obtained from the four possible counts or states are briefly discussed in this paper.