In order to evaluate the performance of a classification model, we have to draw a roc curve based on fpr and tpr. In this tutorial, we will introduce you how to do.
How to draw roc curve in python?
In order to draw a roc curve, we should compute fpr and far. In python, we can use sklearn.metrics.roc_curve() to compute.
Understand sklearn.metrics.roc_curve() with Examples – Sklearn Tutorial
After we have got fpr and tpr, we can drwa roc using python matplotlib.
Here is the full example code:
from matplotlib import pyplot as plt
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)
plt.plot(fpr,tpr, lw=1.5, label="AUC=%.3f)"%auc_value)
plt.xlabel("FPR",fontsize=15)
plt.ylabel("TPR",fontsize=15)
plt.title("ROC")
plt.legend(loc="lower right")
plt.show()Run this code, we will see:
