Thanks for all the suggestions. I will try them and let you know.
El 10/09/14 16:46, "Andy" escribió:
>On 09/10/2014 09:07 AM, Gael Varoquaux wrote:
>> How are you measuring your errors? If you are using the zero-one loss
>> (accuracy score), you are taking in account only the binary decisions,
On 09/10/2014 09:07 AM, Gael Varoquaux wrote:
> How are you measuring your errors? If you are using the zero-one loss
> (accuracy score), you are taking in account only the binary decisions,
> and not a possible decision function. I have found that in the situation
> of unbalanced classes, it could
How are you measuring your errors? If you are using the zero-one loss
(accuracy score), you are taking in account only the binary decisions,
and not a possible decision function. I have found that in the situation
of unbalanced classes, it could be useful to threshold the decision
function at a dif
ustache DIEMERT
>> Responder a: "scikit-learn-general@lists.sourceforge.net" <
>> scikit-learn-general@lists.sourceforge.net>
>> Fecha: martes, 9 de septiembre de 2014 16:33
>> Para: "scikit-learn-general@lists.sourceforge.net" <
>> scikit-learn-gene
tiembre de 2014 16:33
> Para: "scikit-learn-general@lists.sourceforge.net" <
> scikit-learn-general@lists.sourceforge.net>
> Asunto: Re: [Scikit-learn-general] SVC and unbalanced dataset
>
> besides class weights you may try to downsample your negativ
@lists.sourceforge.net>>
Fecha: martes, 9 de septiembre de 2014 16:33
Para:
"scikit-learn-general@lists.sourceforge.net<mailto:scikit-learn-general@lists.sourceforge.net>"
mailto:scikit-learn-general@lists.sourceforge.net>>
Asunto: Re: [Scikit-learn-general] SVC and
besides class weights you may try to downsample your negative examples.
E/
2014-09-09 14:32 GMT+02:00 ZORAIDA HIDALGO SANCHEZ <
zoraida.hidalgosanc...@telefonica.com>:
> Dear all,
>
> I am trying to classify a dataset with a binary target. Number of positive
> instances represents only the 3% of
Dear all,
I am trying to classify a dataset with a binary target. Number of positive
instances represents only the 3% of the total instances. I have tried
using SVC with neither auto_weight nor sample_weight and the confusion
matrix shows that all instances are classified as negative. However, if
