Thanks. The code is provided here:
https://github.com/scikit-learn-contrib/imbalanced-learn/issues/537
Best regards,
On Thu, Jan 24, 2019 at 7:15 PM Guillaume Lemaître
wrote:
> You should open a ticket on imbalanced-learn GitHub issue. This is easier
> to post a reproducible example and for us
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:
>
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
As stated in the doc, categorical_features are the indices of the categorical
column and not the name of the columns. This is similar to the one hot encoder
API.
Sent from my phone - sorry to be brief and potential misspell.
___
scikit-learn
Dear Mr. Lemaitre
Thanks a lot for sharing your time and knowledge. Unfortunately, it throws
the following error:
Traceback (most recent call last):
119
File
"D:/mifs-master_2/MU/learning-from-imbalanced-classes-master/learning-from-imbalanced-classes-master/continuous/Final
SMOTENC will internally one hot encode the features, generate new features,
and finally decode.
So you need to do something like:
from imblearn.pipeline import make_pipeline, Pipeline
num_indices1 = list(X.iloc[:,np.r_[0:94,95,97,100:123]].columns.values)
cat_indices1 =
Dear Scikit-learners
Hi.
I would greatly appreciate if you could let me know how to use SMOTENC. I
wrote:
num_indices1 = list(X.iloc[:,np.r_[0:94,95,97,100:123]].columns.values)
cat_indices1 = list(X.iloc[:,np.r_[94,96,98,99,123:160]].columns.values)
print(len(num_indices1))
