[scikit-learn] Scikit Learn Random Classifier - TPR and FPR plotted on matplotlib

[Dale T Smith]

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 | dale.t.smith at macys.com

From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com at python.org] 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]
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