I want to do customized clustering algo for my datasets, that's cuz I don't
want to try every algo and its hyperparameters.
I though I just define the default range of import hyperparameters ex:
number of cluster in K-means.
I want to iterate some possible clutering alog like K-means, DBSCAN,
AP.
GridSearchCV is meant for tuning hyperparameters of a model over some ranges of
configurations and parameter values. Like the documentation explains:
https://scikit-learn.org/stable/modules/grid_search.html
(and it also has some examples)
The (e.g. 10-fold) cross-validation as measure of accura
Thanks. Unfortunately, now the error is:
ValueError: Some of the categorical indices are out of range. Indices
should be between 0 and 160.
Best regards,
On Sun, Jan 20, 2019 at 8:31 PM S Hamidizade wrote:
> Dear Scikit-learners
> Hi.
>
> I would greatly appreciate if you could let me know how t
You should open a ticket on imbalanced-learn GitHub issue. This is easier
to post a reproducible example and for us to test it.
>From the error message, I can understand that you have 161 features and
require a feature above the index 160.
On Thu, 24 Jan 2019 at 16:19, S Hamidizade wrote:
> Th
Maybe the suitable way is try-and-error?
What I'm interesting is that my datasets is very huge and I can't try
number of cluster from 1 to N if I have N samples
That cost too much time for me.
Maybe I should define the initial number of cluster based on execution time?
Then analyze the next step
