What are the thresholds in ROC curve?

What are the thresholds in ROC curve?

What Are ROC Curves? A useful tool when predicting the probability of a binary outcome is the Receiver Operating Characteristic curve, or ROC curve. It is a plot of the false positive rate (x-axis) versus the true positive rate (y-axis) for a number of different candidate threshold values between 0.0 and 1.0.

What is represented in Y axis of ROC curve?

An ROC curve shows the relationship between clinical sensitivity and specificity for every possible cut-off. The y-axis showing sensitivity (= true positive fraction = TP/(TP+FN))

What are the label at X axis and Y axis in the ROC curve?

The horizontal axis shows the feature x and the vertical axis shows the label which can be positive or negative. The blue circles are the positives and the red circles are the negatives in this figure. This is our training data set and each point is called an example.

Which is the correct threshold for the ROC curve?

In the last graph example, where the threshold is at 1: All positives were miss-classified, therefore TPR = 0% All negatives were correctly classified, hence FPR = 0% Overall, we can see this is a trade-off. As we increase our threshold, we’ll be better at classifying negatives, but this is at the expense of miss-classifying more positives

How is the ROC curve calculated on the Y axis?

The ROC curve is computed just by placing the decision threshold at different measurement values, and computing for each value the proportion of measurements that contain the signal and are correctly classified as such (i.e., they are larger than the threshold), which will be plotted on the y-axis.

How does the ROC curve relate to TPR?

The ROC curve shows a trade-off between TPR and FPR (or false negatives and false positives). It plots TPR vs FPR at different thresholds. If we lower the classification threshold, we will classify more observations as positive, increasing True Positives. But this will cause even the false positives to increase.

Why are ROC curves closer to the diagonal?

A poor classifier (like Classifier 2 below) will not able to distinguish the two classes well and therefore its ROC curve will be closer to the diagonal. Implying lower TPR rates (more false negatives) and higher FPR rates (more false positives). This leads us to the concept of AUC or Area Under the Curve discussed in point c.