Experienced machine learning people usually start by trying to exactly replicate what the paper did, using exactly the same data, and exactly the same methods, and if possible, even exactly the same software. It is very comforting if you can do this, because you can then go ahead and make changes, secure in the knowledge that the reasons for any changes in output are due to the changes you made, and not to small mistakes in data preparation, tool arguments or something.
So it is a good plan to start by seeing whether you can arrange to have the same setup as the writers of the original paper. It is polite to try to do this on your own if you can (and the effort is a useful learning experience, usually) Maybe the data and/or software is publicly available. If not, authors are often willing and able to share material privately, so you could write to them and ask for help. Since you are showing interest in their work, they will want to help if they can. And if they do, you have the beginnings of a useful personal connection. The only pre-condition is that you should (again as matter of politeness) first make an honest attempt to replicate their work, and make sure you really understand it. After you have done that, asking for help is appropriate. Generally speaking, the time to ask questions on a public forum like scikit-learn is also after you have done all you can to solve your problem. Best wishes Chris On 1 June 2016 at 13:05, muhammad waseem <[email protected]> wrote: > It's not the same data (different locations) but I have tried to use same > input and output variables. > > Thanks > Waseem > > On Wed, Jun 1, 2016 at 12:02 PM, Andrew Holmes <[email protected]> > wrote: > >> A previous commenter asked about the published research you mentioned in >> which this was working ok. If you’re using the same data as them, could you >> try to replicate their results first? >> >> Best wishes >> Andrew >> >> @andrewholmes82 <http://twitter.com/andrewholmes82> >> >> >> >> >> >> >> >> >> On 31 May 2016, at 20:05, muhammad waseem <[email protected]> >> wrote: >> >> 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 <[email protected] >> > 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 <[email protected]> >>> 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 < >>> [email protected]> 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 <[email protected]> >>>> 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 <[email protected]> >>>> 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 >>>> [email protected] >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> >>>> >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> [email protected] >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> >>> _______________________________________________ >>> scikit-learn mailing list >>> [email protected] >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> [email protected] >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> >> >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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