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

[Stuart Reynolds]

You're looking at a tiny subset of the possible cutoff thresholds for this
classifier.
Lower thresholds will give higher tot at the expense of tpr.
Usually, AUC is computed at the integral of this graph over the whole range
of FPRs (from zero to one).

If you have your classifier output probabilities or activations, the
maximum and minimum of these values will tell you what the largest and
smallest thresholds should be. Scikit also has a function to directly
receive the activations and true classes and compute the AUC and tpr/fpr
curve.

On Wed, Dec 14, 2016 at 5:12 AM Dale T Smith <Dale.T.Smith at macys.com> wrote:

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