I think you need to look at the examples.
__________________________________________________________________________________________________________________________________________ Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science 5985 State Bridge Road, Johns Creek, GA 30097 | [email protected] From: scikit-learn [mailto:[email protected]] On Behalf Of Debabrata Ghosh Sent: Wednesday, December 14, 2016 3:13 AM To: Scikit-learn user and developer mailing list Subject: [scikit-learn] Scikit Learn Random Classifier - TPR and FPR plotted on matplotlib ⚠ EXT MSG: Hi All, I have run scikit-learn Random Forest Classifier algorithm against a dataset and here is my TPR and FPR against various thresholds: [Inline image 1] Further I have plotted the above values in matplotlib and am getting a very low AUC. Here is my matplotlib code. Can I understand the interpretation of the graph from you please.Is my model Ok or is there something wrong ? Appreciate for a quick response please. import matplotlib.pyplot as plt import numpy as np from sklearn import metrics plt.title('Receiver Operating Characteristic') plt.ylabel('True Positive Rate') plt.xlabel('False Positive Rate') fpr = [0.0002337345394340,0.0001924870472260,0.0001626973851550,0.0000950977673794, 0.0000721826427097,0.0000538505429739,0.0000389557119386,0.0000263523933702, 0.0000137490748018] tpr = [0.19673638244100000000,0.18984141576600000000,0.18122270742400000000, 0.17055510860800000000,0.16434892541100000000,0.15789473684200000000, 0.15134451850100000000,0.14410480349300000000,0.13238336014700000000] roc_auc = metrics.auc(fpr, tpr) plt.plot([0, 1], [0, 1],'r--') plt.plot(fpr, tpr, 'bo-', label = 'AUC = %0.9f' % roc_auc) plt.legend(loc = 'lower right') plt.show() [Inline image 2] * This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening attachments.
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