[scikit-learn] suggested classification algorithm

Thu Nov 17 09:10:38 EST 2016

The problem with your analysis is it doesn’t include anything but features. You may want to look at Nina Zumel and John Mount’s work on y-aware PCR and PCA, as well as y-aware feature scaling.

http://www.win-vector.com/blog/2016/05/pcr_part1_xonly/

http://www.win-vector.com/blog/2016/05/pcr_part2_yaware/

http://www.win-vector.com/blog/2016/06/y-aware-scaling-in-context/

__________________________________________________________________________________________________________________________________________
Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science
5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith at macys.com

From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com at python.org] On Behalf Of Thomas Evangelidis
Sent: Thursday, November 17, 2016 9:01 AM
To: Scikit-learn user and developer mailing list
Subject: Re: [scikit-learn] suggested classification algorithm

⚠ EXT MSG:

Guys thank you all for your hints! Practical experience is irreplaceable that's why I posted this query here. I could read all week the mailing list archives and the respective internet resources but still not find the key info I could potentially get by someone here.

I did PCA on my training set (this one has 24 positive and 1278 negative observation) and projected the 19 features on the first 2 PCs, which explain 87.6 % of the variance in the data. Does this plot help to decide which classification algorithms and/or over- or under-sampling would be more suitable?

https://dl.dropboxusercontent.com/u/48168252/PCA_of_features.png

thanks for your advices
Thomas

On 16 November 2016 at 22:20, Sebastian Raschka <se.raschka at gmail.com<mailto:se.raschka at gmail.com>> wrote:
Yeah, there are many useful resources and implementations scattered around the web. However, a good, brief overview of the general ideas and concepts would be this one, for example: http://www.svds.com/learning-imbalanced-classes/

> On Nov 16, 2016, at 3:54 PM, Dale T Smith <Dale.T.Smith at macys.com<mailto:Dale.T.Smith at macys.com>> wrote:
>
> Unbalanced class classification has been a topic here in past years, and there are posts if you search the archives. There are also plenty of resources available to help you, from actual code on Stackoverflow, to papers that address various ideas. I don’t think it’s necessary to repeat any of this on the mailing list.
>
>
> __________________________________________________________________________________________________________________________________________
> Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science
> 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith at macys.com<mailto:dale.t.smith at macys.com>
>
> From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith<mailto:scikit-learn-bounces%2Bdale.t.smith>=macys.com at python.org<mailto:macys.com at python.org>] 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
>
> ⚠ EXT MSG:
> 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 Evangelidis <tevang3 at gmail.com<mailto:tevang3 at gmail.com>> 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 but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM:
>
> http://scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_001.png
>
> However, I assume that Nearest Neighbors would not be effective in my case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first.
>
> thanks in advance
> Thomas
>
>
> --
> ======================================================================
> Thomas Evangelidis
> Research Specialist
> CEITEC - Central European Institute of Technology
> Masaryk University
> Kamenice 5/A35/1S081,
> 62500 Brno, Czech Republic
>
> email: tevang at pharm.uoa.gr<mailto:tevang at pharm.uoa.gr>
>           tevang3 at gmail.com<mailto:tevang3 at gmail.com>
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> Fernando Marcos Wittmann
> MS Student - Energy Systems Dept.
> School of Electrical and Computer Engineering, FEEC
> University of Campinas, UNICAMP, Brazil
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======================================================================

Thomas Evangelidis

Research Specialist
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/1S081,
62500 Brno, Czech Republic

email: tevang at pharm.uoa.gr<mailto:tevang at pharm.uoa.gr>

          tevang3 at gmail.com<mailto:tevang3 at gmail.com>

website: https://sites.google.com/site/thomasevangelidishomepage/

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