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

[muhammad waseem]

I have tried Random forest (with gridseacrhCV) but did not get good
results.

On Tue, May 31, 2016 at 7:18 PM, Jacob Schreiber <jmschreiber91 at gmail.com>
wrote:

> Using the same feature set? How well do other estimators work? (Linear
> regression, gradient boosting, etc...)
>
> On Tue, May 31, 2016 at 11:10 AM, muhammad waseem <
> m.waseem.ahmad at gmail.com> wrote:
>
>> This problem has been solved in the literature before, I can post papers.
>>
>> On Tue, May 31, 2016 at 7:07 PM, Jacob Schreiber <jmschreiber91 at gmail.com
>> > wrote:
>>
>>> Do you have any other baselines which you can compare to? It might be
>>> helpful in seeing if this is a problem which can be learned.
>>>
>>> On Tue, May 31, 2016 at 10:47 AM, muhammad waseem <
>>> m.waseem.ahmad at gmail.com> wrote:
>>>
>>>> Thanks for your reply. I have day, month, hour, temp, relative
>>>> humidity, Wind speed as my input variables. I can't think of any other
>>>> dependant variables. It is quite strange to me that I don't get results
>>>> after using these input variables.
>>>>
>>>> On Tue, May 31, 2016 at 4:59 PM, 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>
>>>>> 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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>>>>> scikit-learn at python.org
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>>>>>
>>>>>
>>>>>
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>>>>>
>>>>
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