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

On 31 May 2016, at 20:05, muhammad waseem <m.waseem.ahmad@gmail.com> 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 <andrewholmes82@icloud.com> wrote:

Is the training set unbalanced between high and low values? Ie, many more of the high ones?

Best wishes

Andrew

@andrewholmes82

On 31 May 2016, at 20:00, muhammad waseem <m.waseem.ahmad@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 <andrewholmes82@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

On 31 May 2016, at 16:59, Andrew Holmes <andrewholmes82@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

On 31 May 2016, at 16:55, muhammad waseem <m.waseem.ahmad@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

_______________________________________________
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