I try to balance it out, the dataset is very periodic type (similar behaviour in an year)
On Tue, May 31, 2016 at 8:01 PM, Andrew Holmes <andrewholme...@icloud.com> wrote: > Is the training set unbalanced between high and low values? Ie, many more > of the high ones? > > Best wishes > Andrew > > @andrewholmes82 <http://twitter.com/andrewholmes82> > > > > > > > > > On 31 May 2016, at 20:00, muhammad waseem <m.waseem.ah...@gmail.com> > wrote: > > Yes, it has poor performance (higher errors) on lower values. > I have tried random forest but as I mentioned it did not give good results > either, I can try SVR. > > Kindest Regards > Waseem > > On Tue, May 31, 2016 at 6:54 PM, Andrew Holmes <andrewholme...@icloud.com> > wrote: > >> When you say it’s not learning ‘lower values’, does that mean the model >> has good predictions on high values in the test set, but poor performance >> on the low ones? >> >> Have you tried simpler models like tree, random forest and svm as a >> benchmark? >> >> Best wishes >> Andrew >> >> @andrewholmes82 <http://twitter.com/andrewholmes82> >> >> >> >> >> >> >> >> >> On 31 May 2016, at 16:59, Andrew Holmes <andrewholme...@icloud.com> >> wrote: >> >> If the problem is that it’s confusing day and night, are you including >> time of day as a parameter? >> >> Best wishes >> Andrew >> >> @andrewholmes82 <http://twitter.com/andrewholmes82> >> >> >> >> >> >> >> >> >> On 31 May 2016, at 16:55, muhammad waseem <m.waseem.ah...@gmail.com> >> wrote: >> >> Hi All, >> I am trying to train an ANN but until now it is not learning the lower >> values of the training sample. I have tried using different python >> libraries to train ANN. The aim is to predict solar radiation from other >> weather parameters (regression problem). I think the ANN is confusing lower >> values (winter/cloudy days) with the night-time values (probably). I have >> tried the following but none of them worked; >> >> 1. Scaling data between different values e.g. [0,1],[-1,1] >> 2. Standardising data to have zero mean and unit variance >> 3. Shuffling the data >> 4. Increasing the training samples (from 3 years to 10 years) >> 5. Using different train functions >> 6. Trying different transfer functions >> 7. Using few input variables >> 8. Varying hidden layers and hidden layers' neurons >> >> Any idea what could be wrong or any directions to try? >> >> Thanks >> Kindest Regards >> Waseem >> _______________________________________________ >> 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 >> >> > _______________________________________________ > 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 > >
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