Correspondence between score distributions and the ROC curve[br]([url=https://en.wikipedia.org/wiki/Receiver_operating_characteristic]https://en.wikipedia.org/wiki/Receiver_operating_characteristic[/url])[br][list][*]d (x-axis) is the score returned by a binary classifier[/*][*]The red and blue hatchings are distributions of the scores of the positive and negative truths[/*][*]Let the distributions be normal, move "[b]<-->[/b]" to adjust the location and scale[/*][*]d* is a threshold setting to separate the mixing of the positive and negative classifications[/*][*]The blue shaded region (d < d*) is the distribution of false positive (FP) scores[/*][*]The red shaded region (d > d*) is the distribution of false negative (FN) scores[/*][*]The area of the blue shaded region yields the false positive rate FPR[/*][*]The area of the red hatching less the area of the red shade yields the truth positive rate TPR[/*][*]The ROC curve is the plot of TPR vs FPR as function of the threshold setting d*[/*][*]Move d* on Graph to trace the ROC curve on Graph2 [/*][/list][size=150][i]Question[/i]: [i]Find an optimal threshold setting[/i] d*[/size]