Hello Thomas, I don't personally know of any algorithm that works on collections of groupings, but why not first test a simple control model, meaning can you achieve a satisfactory model by simply concatenating all 48 scores per sample and building a forest the standard way? If not, what context or reasons dictate that the groupings need to stay retained as you have presented them?
Hope this helps, J.B. 2016-12-01 22:05 GMT+09:00 Thomas Evangelidis <teva...@gmail.com>: > Sorry, the previous email was incomplete. Below is how the grouped data > look like: > > > Group1: > score1 = [0.56, 0.34, 0.42, 0.12, 0.08, 0.21, ...] > score2 = [0.34, 0.27, 0.24, 0.05, 0.13, 0,14, ...] > y=[1,1,1,0,0,0, ...] # 1 indicates "active" and 0 "inactive" > > Group2: > score1 = [0.34, 0.38, 0.48, 0.18, 0.12, 0.19, ...] > score2 = [0.28, 0.41, 0.34, 0.13, 0.09, 0,1, ...] > y=[1,1,1,0,0,0, ...] # 1 indicates "active" and 0 "inactive" > > ...... > Group24: > score1 = [0.67, 0.54, 0.59, 0.23, 0.24, 0.08, ...] > score2 = [0.41, 0.31, 0.28, 0.23, 0.18, 0,22, ...] > y=[1,1,1,0,0,0, ...] # 1 indicates "active" and 0 "inactive" > > > On 1 December 2016 at 14:01, Thomas Evangelidis <teva...@gmail.com> wrote: > >> 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/ >> >> > > > -- > > ====================================================================== > > 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 > >
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
