Yeah that would probably be best :)

On 04/11/2015 07:04 AM, Joel Nothman wrote:
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 <mailto: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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