[scikit-learn] Artificial neural network not learning lower values of the training sample

[muhammad waseem]

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 at 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>
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> On 31 May 2016, at 20:00, muhammad waseem <m.waseem.ahmad at 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 at 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>
>>
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>> On 31 May 2016, at 16:59, Andrew Holmes <andrewholmes82 at 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>
>>
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>> On 31 May 2016, at 16:55, muhammad waseem <m.waseem.ahmad at 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
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