MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -An ROC curve plots TPR vs. FPR at different classification thresholds. Lowering the classification threshold classifies more items as positive, thus increasing both False Positives and True Positives. The following figure shows a typical ROC curve.

Detailed explanation-2: -To plot the ROC curve, we need to calculate the TPR and FPR for many different thresholds (This step is included in all relevant libraries as scikit-learn ). For each threshold, we plot the FPR value in the x-axis and the TPR value in the y-axis. We then join the dots with a line. That’s it!

Detailed explanation-3: -The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The true-positive rate is also known as sensitivity, recall or probability of detection.

Detailed explanation-4: -You can divide the space into 2 parts: a triangle and a trapezium. The triangle will have area TPR*FRP/2, the trapezium (1-FPR)*(1+TPR)/2 = 1/2-FPR/2 + TPR/2-TPR*FPR/2 . The total area is 1/2-FPR/2 + TPR/2 . This is how you can get it, having just 2 points.

Detailed explanation-5: -An ROC curve is a plot of sensitivity on the y axis against (1−specificity) on the x axis for varying values of the threshold t. The 45° diagonal line connecting (0, 0) to (1, 1) is the ROC curve corresponding to random chance.