In [1]:

    

%matplotlib inline
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import random


    

In [2]:

    

actual = [1,1,1,0,0,0]
predictions = [0.9,0.9,0.9,0.1,0.1,0.1]


    

In [3]:

    

false_positive_rate, true_positive_rate, thresholds = roc_curve(actual, predictions)
roc_auc = auc(false_positive_rate, true_positive_rate)


    

In [4]:

    

plt.title('Receiver Operating Characteristic')
plt.plot(false_positive_rate, true_positive_rate, 'b',
label='AUC = %0.2f'% roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.2])
plt.ylim([-0.1,1.2])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()


    

In [ ]:

    

actual = [1,1,1,0,0,0]
predictions = [0.9,0.9,0.1,0.1,0.1,0.1]


    

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