dear jack,
do you have an equal number of samples for all classes? the default metric
is a weighted average that takes into account imbalance across samples. see:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/metrics.py#L583
cheers,
satra
On Sat, Oct 6, 2012 at 11:32 AM, Jack Alan <[email protected]> wrote:
> Hi everybody,
>
> I'm using Sklearn for text classification. After training and testing,
> Sklearn has the ability to calculate interesting metrics, i.e. precision
> (P), recall (R) and f1-measure (F1).
>
> My question is:
> Suppose having 9 different classes and for each class we can have all P, R
> and F1. I saw that, sklearn calculate in the bottom of the result's table
> what it is called Average. I wounder this average how is calculated since I
> tried to devide the sum over the total number of classes and I got
> different average?
>
> I gave you a real example below:
>
> Classes:
> Class1 P
> 0.62 R
> 0.69
> F
> 0.65 Class2 0.83 0.76 0.79 Class3 0.47 0.4 0.43 Class4 0.72 0.66 0.69
> Class5 0.88 0.68 0.77 Class6 0.77 0.91 0.83 Class7 0.82 0.72 0.77
> Class8 0.78 0.78 0.78 Class9 0.88 0.7 0.78 ---------------------
> Sklearn
> - AVG 0.77 0.76 0.76 what I got AVG: 0.75
> 0.70 0.72
>
> Can anyone pinpoint me to the right answer? feeling struggled.
>
> Jack
>
>
>
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