[scikit-learn] anti-correlated predictions by SVR

[Thomas Evangelidis]

Greetings,

I don't know if anyone encountered this before, but sometimes I get
anti-correlated predictions by the SVR I that am training. Namely, the
Pearson's R and Kendall's tau are negative when I compare the predictions
on the external test set with the true values. However, the SVR predictions
on the training set have positive correlations with the experimental values
and hence I can't think of a way to know in advance if the trained SVR will
produce anti-correlated predictions in order to change their sign and avoid
the disaster. Here is an example of what I mean:

Training set predictions: R=0.452422, tau=0.333333
External test set predictions: R=-0.537420, tau-0.300000

Obviously, in a real case scenario where I wouldn't have the external test
set I would have used the worst observation instead of the best ones. Has
anybody any idea about how I could prevent this?

thanks in advance
Thomas



-- 

======================================================================

Dr Thomas Evangelidis

Post-doctoral Researcher
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/2S049,
62500 Brno, Czech Republic

email: tevang at pharm.uoa.gr

          tevang3 at gmail.com


website: https://sites.google.com/site/thomasevangelidishomepage/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20170926/c9efa33f/attachment.html>