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

```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

```_______________________________________________