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