Greetings,
I know this is a recurrent question, but I would like to use my own loss
function either in a MLPRegressor or in an SVR. For the MLPRegressor case
so far my conclusion was that it is not possible unless you modify the
source code. On the other hand, for the SVR I was looking at setting custom
kernel functions. But I am not sure if this is the same thing. Could
someone please clarify this to me? Finally, I read about the "scoring"
parameter is cross-validation, but this is just to select a Regressor that
has been trained already with the default loss function, so it would be
harder to find one that minimizes my own loss function.
For the record, my loss function is the centered root mean square error.
Thanks in advance for any advice.
--
======================================================================
Dr Thomas Evangelidis
Post-doctoral Researcher
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/2S049,
62500 Brno, Czech Republic
email: tev...@pharm.uoa.gr
teva...@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
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