Machine Learning, Investment, AI, Sklearn, Quant.
print('Classification Report:') print(report) # Plot ROC curve fpr, tpr, _ = roc_curve(y_1, y_pred_prob) plt.figure() plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0, 1], [0, 1], 'k--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate')