>
> Thanks for this -- looks useful. I had to write something similar (for
>> the binary case) and wish scikit had something like this.
>
>
> Which part of it? I'm not entirely sure I understand what the core
> functionality is.
>
>
I think the core efficiently evaluating the full set of metrics
Is that Jet?!
https://www.youtube.com/watch?v=xAoljeRJ3lU
;)
On 6/4/18 11:56 AM, Brown J.B. via scikit-learn wrote:
Hello community,
I wonder if there's something similar for the binary class
case where,
the prediction is a real value (activation) and from this we
Hello community,
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, ...)
>>
> AUC and AP are by
On 5/31/18 1:26 PM, Stuart Reynolds 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.
Which part of it? I'm not entirely sure I understand what the core
functionality is.
I wonder if there's
Hi Stuart
Thanks ;-)
Activation threshold is in our plan and will be added in next release (in theĀ
next few weeks)
Best RegardsSepand Haghighi
On Thursday, May 31, 2018, 9:56:43 PM GMT+4:30, Stuart Reynolds
wrote:
Hi Sepand,
Thanks for this -- looks useful. I had to write