[scikit-learn] PyCM: Multiclass confusion matrix library in Python

[Sepand Haghighi]

Hi Stuart
Thanks ;-)
Activation threshold is in our plan and will be added in next release (in the next few weeks)


Best RegardsSepand Haghighi 

    On Thursday, May 31, 2018, 9:56:43 PM GMT+4:30, Stuart Reynolds <stuart at stuartreynolds.net> wrote:  
 
 Hi Sepand,

Thanks for this -- looks useful. I had to write something similar (for
the binary case) and wish scikit had something like this.

I wonder if there's something similar for the binary class case where,
the prediction is a real value (activation) and from this we can also
derive
 - CMs for all prediction cutoff (or set of cutoffs?)
 - scores over all cutoffs (AUC, AP, ...)

For me, in analyzing (binary class) performance, reporting scores for
a single cutoff is less useful than seeing how the many scores (tpr,
ppv, mcc, relative risk, chi^2, ...) vary at various false positive
rates, or prediction quantiles.
Does your library provide any tools for the binary case where we add
an activation threshold?

Thanks again for releasing this and providing pip packaging.
- Stuart


On Thu, May 31, 2018 at 6:05 AM, Sepand Haghighi via scikit-learn
<scikit-learn at python.org> wrote:
> PyCM is a multi-class confusion matrix library written in Python that
> supports both input data vectors and direct matrix, and a proper tool for
> post-classification model evaluation that supports most classes and overall
> statistics parameters. PyCM is the swiss-army knife of confusion matrices,
> targeted mainly at data scientists that need a broad array of metrics for
> predictive models and an accurate evaluation of large variety of
> classifiers.
>
> Github Repo : https://github.com/sepandhaghighi/pycm
>
> Webpage : http://pycm.shaghighi.ir/
>
> JOSS Paper : https://doi.org/10.21105/joss.00729
>
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