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In a ROC curve the true positive rate (Sensitivity) is plotted in function of the false positive rate (100-Specificity) for different cut-off points of a parameter. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. The area under the ROC curve (AUC) is a measure of how well a parameter can distinguish between two diagnostic groups (diseased/normal). Logistic regression is often used as a classifier to assign class labels to a person, item, or transaction based on the predicted probability provided by the model. In the Churn example, a customer can be classified with the label called Churn if the logistic model predicts a high probability that the customer will churn. Otherwise, a Remain label is assigned to the customer. Commonly, 0.5 is used as the default probability threshold to distinguish between any two class labels. However, any threshold value can be used depending on the preference to avoid false positives (for example, to predict Churn when actually the customer will Remain) or false negatives (for example, to predict Remain when the customer will actually Churn).

 

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