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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