3.2.10 Introduction to Logistical Regression - Video 6 ROC Curves
Picking a good threshold value is often challenging. A Receiver Operator Characteristic curve, or ROC curve, can help you decide which value of the threshold is best. The ROC curve for our problem is shown on the right of this slide. The sensitivity, or true positive rate of the model, is shown on the y-axis. And the false positive rate, or 1 minus the specificity, is given on the x-axis. The line shows how these two outcome measures vary with different threshold values. The ROC curve always starts at the point (0, 0). This corresponds to a threshold value of 1. If you have a threshold of 1, you will not catch any poor care cases, or have a sensitivity of 0. But you will correctly label of all the good care cases, meaning you have a false positive rate of 0. The ROC curve always ends at the point (1,1), which corresponds to a threshold value of 0. If you have a threshold of 0, you'll catch all of the poor care cases, or have a sensitivity of 1, but you'll label al...