Yes, I found that too and wished it were published with higher editorial
standards so it could be more readable.
On 23 July 2014 16:48, Dan Haiduc <danuthai...@gmail.com> wrote:
> Here's a comparison of all of them: EVALUATION: FROM PRECISION, RECALL
> AND F-MEASURE TO ROC, INFORMEDNESS, MARKEDNESS & CORRELATION
> <http://dspace2.flinders.edu.au/xmlui/bitstream/handle/2328/27165/Powers%20Evaluation.pdf>
> I warmly recommend MCC, though lots of people still use ROC
>
>
> On Wed, Jul 23, 2014 at 6:09 AM, Joel Nothman <joel.noth...@gmail.com>
> wrote:
>
>> Precision, Recall and F-measure are often contrasted with Accuracy in
>> terms of their handling imbalance. I'm sure I could find a textbook
>> citation, but for an online example Chris Manning thus introduces P/R/F in
>> the imbalanced spam classification problem on coursera:
>> https://class.coursera.org/nlp/lecture/142.
>>
>>
>> On 23 July 2014 11:35, Mathieu Blondel <math...@mblondel.org> wrote:
>>
>>> AUC (area under the roc curve) is commonly used for imbalanced binary
>>> classification problems.
>>> The AUC is the probability that your classifier will rank a positive
>>> sample higher than a negative sample (where the ranking is computed using
>>> the "decision_function" scores).
>>> In scikit-learn, it is implemented in roc_auc_score.
>>>
>>> Mathieu
>>>
>>>
>>> On Wed, Jul 23, 2014 at 12:26 AM, Hamed Zamani <hamedzam...@acm.org>
>>> wrote:
>>>
>>>> Hi,
>>>>
>>>> I am working on a binary classification problem in which both training
>>>> and test data are highly imbalanced. In other words, the number of
>>>> instances available in one class is far more than the other one.
>>>>
>>>> Would you please let me know which evaluation measure is the best one
>>>> to compare different methods in imbalanced situations? Please note that
>>>> predicting the label of instances of the class which contains lower
>>>> instances is really harder than predicting the labels of the other
>>>> instances and I am looking for a evaluation measure which consider this
>>>> issue.
>>>>
>>>> I am wondering if you also provide me a reference for your opinions.
>>>>
>>>> Thanks a lot,
>>>> Best regards,
>>>> Hamed
>>>>
>>>>
>>>>
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>>>
>>>
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>>
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>
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