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<div class="">Hi All, </div>
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I have been seeing d’ used in place of accuracy more and more as a metric of classification performance, but it appears that no one feels the need to describe how they are calculating it. I am used to d’ in the context of ROC characteristics, i.e.
<div class=""><font face="Courier" class="">dprime=scipy.stats.norm.ppf(TPR) - scipy.stats.norm.ppf(FDR)</font>
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<div class="">but I don’t see how that necessarily translates to a classification problem. If I’m training a classifier to discriminate between patterns associated with animate and inanimate objects (as an example), then I will usually get 2 different d’ metrics
depending on if I consider hits to be correct classification of animate objects or correct classification in inanimate objects, and this is underscored by the fact that the ca ‘stats’ gives me a different TPR and FDR for each class in the classification. How
are these two d’ measures supposed to be combined to give me a single metric of classification performance? Is it just implied that the single reported d’ is the average of the two d' associated with the two-way classification, and everyone just knows that’s
always the case already? Do I calculate d’ based on averaged TPR and FDR (which gives a different result)? I guess the biggest question is can someone please point me to where the standard way that singular d’ is to be calculated in a classification setting
is spelled out to where no one includes the math in their published papers, because I can’t find it.</div>
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<div class="">Thanks, and I hope no one gets too mad at me for posting this general question in the software support liserv. </div>
<div class="">- Gavin</div>
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<div class="">~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br class="">
Gavin Hanson, B.S.<br class="">
Research Assistant<br class="">
Department of Psychology<br class="">
University of Kansas<br class="">
1415 Jayhawk Blvd., 534 Fraser Hall <br class="">
Lawrence, KS 66045</div>
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