How do you calculate AUC from confusion matrix?

How do you calculate AUC from confusion matrix?

AUC is a Area Under ROC curve.

1. First make a plot of ROC curve by using confusion matrix.
2. Normalize data, so that X and Y axis should be in unity. Even you can divide data values with maximum value of data.
3. Use Trapezoidal method to calculate AUC.
4. Maximum value of AUC is one.

What does AUC score mean?

Area Under the Curve
The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.

How to calculate ROC curve and AUC by hand?

False positive rate (FPR), a.k.a. false alarm rate, fall-out or 1 – specificity, which is defined as F P F P + T N. Intuitively this metric corresponds to the proportion of negative data points that are mistakenly considered as positive, with respect to all negative data points.

What happens if TPR and FPR are zero?

It is easy to see that if the threshold is zero, all our prediction will be positive, so both TPR and FPR will be 1. On the other hand, if the threshold is 1, then no positive prediction will be made, both TPR and FPR will be 0.

How to calculate and use the AUC score?

The AUC score is simply the area under the curve which can be calculated with Simpson’s Rule. The bigger the AUC score the better our classifier is. Given two classifiers A & B, we expect two different ROC curves. Consider the plot below:

What should be the AUC of a classifier?

A perfect classifier would have an AUC of 1. Usually, if your model behaves well, you obtain a good classifier by selecting the value of threshold that gives TPR close to 1 while keeping FPR near 0. It is easy to see that if the threshold is zero, all our prediction will be positive, so both TPR and FPR will be 1.