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

[Andrew Holmes]

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 <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>
> 
> 
> 
> 
> 
> 
> 
> 
>> On 31 May 2016, at 16:55, muhammad waseem <m.waseem.ahmad at gmail.com <mailto: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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