[scikit-learn] top N accuracy classification metric

[Johnson, Jeremiah]

Okay, I didn't see anything equivalent in the issue tracker, so submitted a pull request.


Jeremiah


===============================
Jeremiah W. Johnson, Ph. D
Assistant Professor of Data Science
Analytics Bachelor of Science Program Coordinator
University of New Hampshire
http://linkedin.com/jwjohnson314
________________________________
From: scikit-learn <scikit-learn-bounces+jeremiah.johnson=unh.edu at python.org> on behalf of Joel Nothman <joel.nothman at gmail.com>
Sent: Saturday, January 21, 2017 5:52 AM
To: Scikit-learn user and developer mailing list
Subject: Re: [scikit-learn] top N accuracy classification metric

There are metrics with that kind of input in sklearn.metrics.ranking. I don't have the time to look them up now, but there have been proposals and PRs for similar ranking metrics. Please search the issue tracker for related issues. Thanks, Joel

On 21 January 2017 at 06:16, Johnson, Jeremiah <Jeremiah.Johnson at unh.edu<mailto:Jeremiah.Johnson at unh.edu>> wrote:
Hi all,

It's common to use a top-n accuracy metric for multi-class classification problems, where for each observation the prediction is the set of probabilities for each of the classes, and a prediction is top-N accurate if the correct class is among the N highest predicted probability classes. I've written a simple implementation, but I don't think it quite fits the sklearn api. Specifically, _check_targets objects to the the continuous-multioutput format of the predictions for a classification task. Is there any interest in including a metric like this? I'd be happy to submit a pull request.

Jeremiah


_______________________________________________
scikit-learn mailing list
scikit-learn at python.org<mailto:scikit-learn at python.org>
https://mail.python.org/mailman/listinfo/scikit-learn<https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.python.org_mailman_listinfo_scikit-2Dlearn&d=DwMFaQ&c=c6MrceVCY5m5A_KAUkrdoA&r=hQNTLb4Jonm4n54VBW80WEzIAaqvTOcTEjhIkrRJWXo&m=3qvCQaOyx8sDfjACeJj0PSYXii1EE9f5SNgx8OqqcQ4&s=g6ryDsG0_xttmZORL3MQoAFwj01miH300Hy2cBmRgg8&e=>


-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20170126/363a3c03/attachment-0001.html>