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')