Greetings
I have grouped data which are divided into actives and inactives. The
features are two different types of normalized scores (0-1), where the
higher the score the most probable is an observation to be an "active". My
data look like this:
Group1:
score1 = [0.56, 0.34, 0.42, 0.12, 0.08, 0.21, ...]
score2 = [
y=[1,1,1,0,0,0, ...]
Group2:
score1 = [0
score2 = [
y=[1,1,1,1,1]
......
Group24:
score1 = [0
score2 = [
y=[1,1,1,1,1]
I searched in the documentation about treatment of grouped data, but the
only thing I found was how do do cross-validation. My question is whether
there is any special algorithm that creates random forests from these type
of grouped data.
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: tev...@pharm.uoa.gr
teva...@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
