# Re: [scikit-learn] random forests using grouped data

```Hello Thomas,

I don't personally know of any algorithm that works on collections of
groupings, but why not first test a simple control model, meaning
can you achieve a satisfactory model by simply concatenating all 48 scores
per sample and building a forest the standard way?
If not, what context or reasons dictate that the groupings need to stay
retained as you have presented them?```
```
Hope this helps,
J.B.

2016-12-01 22:05 GMT+09:00 Thomas Evangelidis <teva...@gmail.com>:

> Sorry, the previous email was incomplete. Below is how the grouped data
> look like:
>
>
> Group1:
> score1 = [0.56, 0.34, 0.42, 0.12, 0.08, 0.21, ...]
> score2 = [0.34, 0.27, 0.24, 0.05, 0.13, 0,14, ...]
> y=[1,1,1,0,0,0, ...]  # 1 indicates "active" and 0 "inactive"
>
> Group2:
> score1 = [0.34, 0.38, 0.48, 0.18, 0.12, 0.19, ...]
> score2 = [0.28, 0.41, 0.34, 0.13, 0.09, 0,1, ...]
> y=[1,1,1,0,0,0, ...]  # 1 indicates "active" and 0 "inactive"
>
> ​......
> Group24​:
> score1 = [0.67, 0.54, 0.59, 0.23, 0.24, 0.08, ...]
> score2 = [0.41, 0.31, 0.28, 0.23, 0.18, 0,22, ...]
> y=[1,1,1,0,0,0, ...]  # 1 indicates "active" and 0 "inactive"
>
>
> On 1 December 2016 at 14:01, Thomas Evangelidis <teva...@gmail.com> wrote:
>
>> Greetings
>>
>> ​I have grouped data which are divided into actives and inactives. The
>> features are two different types of normalized scores (0-1), where the
>> higher the score the most probable is an observation to be an "active". My
>> data look like this:
>>
>>
>> Group1:
>> score1 = [0.56, 0.34, 0.42, 0.12, 0.08, 0.21, ...]
>> score2 = [
>> y=[1,1,1,0,0,0, ...]
>>
>> Group2:
>> ​score1 = [0
>> score2 = [
>> y=[1,1,1,1,1]​
>>
>> ​......
>> Group24​:
>> ​score1 = [0
>> score2 = [
>> y=[1,1,1,1,1]​
>>
>>
>> I searched in the documentation about treatment of grouped data, but the
>> only thing I found was how do do cross-validation. My question is whether
>> there is any special algorithm that creates random forests from these type
>> of grouped data.
>>
>> Thomas
>>
>>
>>
>> --
>>
>> ======================================================================
>>
>> Thomas Evangelidis
>>
>> Research Specialist
>> CEITEC - Central European Institute of Technology
>> Masaryk University
>> Kamenice 5/A35/1S081,
>> 62500 Brno, Czech Republic
>>
>> email: tev...@pharm.uoa.gr
>>
>>           teva...@gmail.com
>>
>>
>>
>>
>
>
> --
>
> ======================================================================
>
> Thomas Evangelidis
>
> Research Specialist
> CEITEC - Central European Institute of Technology
> Masaryk University
> Kamenice 5/A35/1S081,
> 62500 Brno, Czech Republic
>
> email: tev...@pharm.uoa.gr
>
>           teva...@gmail.com
>
>
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn@python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
```
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