ata Science
> > 5985 State Bridge Road, Johns Creek, GA 30097 | [email protected]
> >
> > From: scikit-learn
> > [mailto:[email protected]] On Behalf
> > Of Fernando Marcos Wittmann
> > Sent: Wednesday, November 16, 20
ann
> Sent: Wednesday, November 16, 2016 3:11 PM
> To: Scikit-learn user and developer mailing list
> Subject: Re: [scikit-learn] suggested classification algorithm
>
> ⚠ EXT MSG:
> Three based algorithms (like Random Forest) usually work well for imbalanced
> datasets. You
State Bridge Road, Johns Creek, GA 30097 | [email protected]
>
> From: scikit-learn
> [mailto:[email protected]] On Behalf Of
> Fernando Marcos Wittmann
> Sent: Wednesday, November 16, 2016 3:11 PM
> To: Scikit-learn user and dev
[email protected]
From: scikit-learn
[mailto:[email protected]] On Behalf Of
Fernando Marcos Wittmann
Sent: Wednesday, November 16, 2016 3:11 PM
To: Scikit-learn user and developer mailing list
Subject: Re: [scikit-learn] suggested classification algorithm
Three based algorithms (like Random Forest) usually work well for
imbalanced datasets. You can also take a look at the SMOTE technique (
http://jair.org/media/953/live-953-2037-jair.pdf) which you can use for
over-sampling the positive observations.
On Mon, Nov 14, 2016 at 9:14 AM, Thomas Evangeli
http://contrib.scikit-learn.org/imbalanced-learn/ might be of interest to
you.
On 14 November 2016 at 22:14, Thomas Evangelidis wrote:
> Greetings,
>
> I want to design a program that can deal with classification problems of
> the same type, where the number of positive observations is small bu
