Sebastian Raschka
Sebastian Raschka
Yes, that would be one solution. However, the results would be different. E.g. let's assume we have three features, A and B, C, where A is a categorical feature with...
I actually tried that the other day with a scikit-learn Pipeline, OneHotEncoder, and ColumnTransformer. The technical limitation with using NumPy arrays is that we can't rely on column indices because...
So, what I had in mind the other day was something like this ```python import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.datasets import load_iris from mlxtend.feature_selection import SequentialFeatureSelector...
Hi there. Unfortunately, I don't think #506 fully addressed this issue, so this is not supported yet.
I would like to add more descriptive comments. It's just hard to come up with a good rule here to catch errors related to the above mentioned one-hot encoding approach....
Thanks for sharing! This would be another nice addition for the tutorials.
Hey @NimaSarajpoor, you are right the sequential feature selector could (/should) eventually have a feature group support similar to the exhaustive feature selector. It’s something I was hoping to tackle...
Thanks and no worries about the timeline at all! Currently so many things to catch up with 😅
Thanks for the note! I was looking into this but couldn't find the root cause of this issue in the McNemar files. I think it may have something to do...