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Messages 15-12 deleted by topic administrator between 07-05-2009 02:05 AM and 07-02-2009 02:50 PM |
| Lscsjbwq
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06-25-2009 12:40 AM ET (US)
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Y9lGts comment5 ,
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| ordomrolinot
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08-16-2008 09:46 AM ET (US)
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orrolco
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05-17-2008 05:02 AM ET (US)
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Deleted by topic administrator 05-17-2008 10:16 AM
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| Brin
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12-04-2007 05:59 PM ET (US)
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Hello, nice site :)
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| John Doe
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11-07-2007 07:51 AM ET (US)
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70ae9e4b15b913ee9fd0768b5c10baae
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Messages 6-5 deleted by topic administrator 07-23-2006 02:08 AM |
| Ben Laxton
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10-06-2005 03:28 PM ET (US)
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To follow up on Anup's remark, does the ROC space provide a clear advantage to the f-measure or metircs used for decision tree splitting - i.e. does the unification of these different metrics give more insight? Also, has this been used in some application that would make its strengths apparent?
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| Anup Doshi
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10-06-2005 02:47 PM ET (US)
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It's very interesting that they are able to unify several different metrics through the 3D ROC. But I don't think it is anything more than a theoretical exercise. If the metrics generally used for your sub-field/problem work well enough, why would you bother trying to extrapolate metrics that are consistent over a whole bunch of fields?
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| Erik Murphy-Chutorian
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10-06-2005 02:37 PM ET (US)
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I found this to be a good review of ROC curves, covering a few of the less-common performance measures. It would be interesting to know more of the logic behind choosing one metric over another.
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| Brendan Morris
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10-06-2005 01:48 PM ET (US)
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I really would like to ask some intelligent question but after a first reading I still don't understand anything about this paper. I've limited experience with ROC curves in general and no experience with most of the metrics in the paper. I do like the thought of a unifying structure as it seems that different definitions are misleading when looking at results.
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