# Compute AUC Metric Based on FPR and TPR in Python

By | August 8, 2022

AUC is an important metric to evaluate the performance of a classification model. In this tutorial, we will introduce you how to compute its value.

## What is AUC?

AUC is also called Area Under the Curve. It is the area under the ROC curve. For example:

It represents:

$$AUC = P(P_{positive sample}> P_{negative sample})$$

## How to compute AUC?

In order to compute it, we should know fpr and tpr. We can compute them by sklearn.metrics.roc_curve().

Understand sklearn.metrics.roc_curve() with Examples – Sklearn Tutorial

Then,we can use sklearn.metrics.auc(fpr, tpr) to compute AUC.

For example:

from sklearn.metrics import roc_curve, auc

plt.style.use('classic')

labels = [1,0,1,0,1,1,0,1,1,1,1]
score = [-0.2,0.1,0.3,0,0.1,0.5,0,0.1,1,0.4,1]

fpr, tpr, thresholds = roc_curve(labels,score, pos_label=1)
print(fpr, tpr, thresholds)
auc_value = auc(fpr,tpr)
print(auc_value)

Run this code, we will find auc_value = 0.833