Roc curve python

import sklearn.metrics as metrics
# calculate the fpr and tpr for all thresholds of the classification
probs = model.predict_proba(X_test)
preds = probs[:,1]
fpr, tpr, threshold = metrics.roc_curve(y_test, preds)
roc_auc = metrics.auc(fpr, tpr)

# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()

# method II: ggplot
from ggplot import *
df = pd.DataFrame(dict(fpr = fpr, tpr = tpr))
ggplot(df, aes(x = 'fpr', y = 'tpr')) + geom_line() + geom_abline(linetype = 'dashed')

Source: stackoverflow.com

Similar codes

19 votes139 views6 answers
Pip install django
Python
0 vote126 views3 answers
Pysftp get-r
Python
0 vote132 views1 answer
Identify total number of iframes with Selenium
Python
0 vote119 views1 answer
And condition in redis client
Python
32 votes125 views10 answers
Google translate
Python
0 vote134 views1 answer
How to open excel with more than one sheetpython
Python
2 votes143 views1 answer
Tkinter input popup
Python
4 votes124 views3 answers
Row wise mean pandas
Python
2 votes121 views1 answer
Pass in queryset as filter django
Python
0 vote135 views1 answer
Dalsports
Python