Hi, hm, I think that dropping a column in onehot encoded features is quite uncommon in machine learning practice -- based on the applications and implementations I've seen. My guess is that the onehot encoded features are multicolinear anyway!? There may be certain algorithms that benefit from dropping a column, though (e.g., linear regression as a simple example). For instance, pandas' get_dummies has a "drop_first" parameter ... I think it would make sense to have such a parameter in the onehotencoder as well, e.g., for working with pipelines. Best, Sebastian

On Jun 25, 2017, at 7:48 AM, Parminder Singh <parmsingh129@gmail.com> wrote:

Hy Sci-kittens! :-)

I was doing machine learning a-z course on Udemy, there they told that every time one-hot encoding is done, one of the columns should be dropped as it is like doubling same category twice and redundant to model. I thought if instead of having user find the index and drop it after preprocessing, OneHotEncoder had a drop_one variable, and it automatically removed the last column. What are your thoughts about this? I am new to this community, would like to contribute this myself if it is possible addition.

Thanks, Trion129 _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn