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

[Andrew Holmes]

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.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 <mailto: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 <mailto: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 <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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>> 
> 
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