# Answers for "ROC curve scipy"

8

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.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')``````
Posted by: Guest on July-11-2020
0

scikit learn roc curve

``fpr,tpr = sklearn.metrics.roc_curve(y_true, y_score, average='macro', sample_weight=None)``
Posted by: Guest on May-03-2020
0

scikit learn roc curve

``auc = sklearn.metric.auc(fpr, tpr)``
Posted by: Guest on May-03-2020