sklearn roc curve

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Alessandra
20 Jun 2017
1import sklearn.metrics as metrics
2# calculate the fpr and tpr for all thresholds of the classification
3probs = model.predict_proba(X_test)
4preds = probs[:,1]
5fpr, tpr, threshold = metrics.roc_curve(y_test, preds)
6roc_auc = metrics.auc(fpr, tpr)
7
8# method I: plt
9import matplotlib.pyplot as plt
10plt.title('Receiver Operating Characteristic')
11plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
12plt.legend(loc = 'lower right')
13plt.plot([0, 1], [0, 1],'r--')
14plt.xlim([0, 1])
15plt.ylim([0, 1])
16plt.ylabel('True Positive Rate')
17plt.xlabel('False Positive Rate')
18plt.show()
19
20# method II: ggplot
21from ggplot import *
22df = pd.DataFrame(dict(fpr = fpr, tpr = tpr))
23ggplot(df, aes(x = 'fpr', y = 'tpr')) + geom_line() + geom_abline(linetype = 'dashed')
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Nohan
16 Aug 2018
1   fpr,tpr = sklearn.metrics.roc_curve(y_true, y_score, average='macro', sample_weight=None)
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Fernand
30 Sep 2016
1auc = sklearn.metric.auc(fpr, tpr)
source
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