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

[Jacob Schreiber]

To make a proper ROC curve you need to test all possible thresholds, not
just a subset of them. You can do this easily in sklearn.

import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, roc_auc_score

... <load your data, fit your classifier> ...

y_pred = clf.predict_proba(X)
fpr, tpr, _ = roc_curve(y_true, y_pred)
auc = roc_auc_score(y_true, y_pred)
plt.plot(fpr, tpr, label=auc)

On Wed, Dec 14, 2016 at 8:52 AM, Stuart Reynolds <stuart at stuartreynolds.net>
wrote:

> 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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>> ____________________________________________________________
>> ____________________________________________________________
>> __________________
>>
>>
>> *Dale T. Smith*
>>
>> *|* Macy's Systems and Technology
>>
>> *|* 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]
>>
>> *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
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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
>>
>> 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
>> opening attachments.
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>>
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