Or report macro and micro in classification_report. Micro is equivalent to
accuracy for multiclass without #4287
<https://github.com/scikit-learn/scikit-learn/pull/4287>.
On 10 April 2015 at 01:00, Andreas Mueller <t3k...@gmail.com> wrote:
> Hi Jack.
> You mean in the classification report?
> That give micro-average from looking at the code:
>
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/classification.py#L1265
>
> If you use the f1_score function instead you can give the averaging scheme:
>
> http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score
>
> I wonder if we should rather call precision_recall_fscore_support again
> here with an average option?
>
> Hth,
> Andy
>
>
>
> On 04/09/2015 10:20 AM, Jack Alan wrote:
>
> Hi folks,
>
> I wonder for classification of text documents available on:
>
> http://scikit-learn.org/stable/auto_examples/text/mlcomp_sparse_document_classification.html#example-text-mlcomp-sparse-document-classification-py
>
> What sort of F-measure that has been used? Is it Micro or Macro? and how
> to change the default option to use the other?
>
> ~j
>
>
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