No.
Sent from my phone. Please forgive typos and briefness.
On Jul 26, 2018, 07:28, at 07:28, Raphael C wrote:
>Is it expected that all three linkages options should give the same
>result
>in my toy example?
>
>Raphael
>
>On Thu, 26 Jul 2018 at 06:20 Gael Varoquaux
>
>wrote:
>
>> FeatureAgglom
Is it expected that all three linkages options should give the same result
in my toy example?
Raphael
On Thu, 26 Jul 2018 at 06:20 Gael Varoquaux
wrote:
> FeatureAgglomeration uses the Ward, complete linkage, or average linkage,
> algorithms, depending on the choice of "linkage". These are well
FeatureAgglomeration uses the Ward, complete linkage, or average linkage,
algorithms, depending on the choice of "linkage". These are well
documented in the literature, or on wikipedia.
Gaël
On Thu, Jul 26, 2018 at 06:05:21AM +0100, Raphael C wrote:
> Hi,
> I am trying to work out what, in preci
Hi,
I am trying to work out what, in precise mathematical terms,
[FeatureAgglomeration][1] does and would love some help. Here is some
example code:
import numpy as np
from sklearn.cluster import FeatureAgglomeration
for S in ['ward', 'average', 'complete']:
FA = FeatureAgglo
On Wed, Jul 25, 2018 at 12:36:55PM +0200, Benoît Presles wrote:
> Do you think the problems I have can come from correlated features? Indeed,
> in my dataset I have some highly correlated features.
Yes, in general selecting features conditionally on others is very hard
when features are highly cor
Do you think the problems I have can come from correlated features?
Indeed, in my dataset I have some highly correlated features.
Do you think this could explain why I don't get reproducible and
consistent results?
Thanks for your help,
Ben
Le 24/07/2018 à 23:44, bthirion a écrit :
Univariate
