Do I use dummy encoding or one hot encoding when trying to do regression?

I am trying to do regression for the first time using qualitative and quantitative data using scikit learn.

I want to find correlations between user demographic features like age range, country, gender (all categorical), account age (numerical) and their overall listening time (numerical).

I am considering one-hot-encoding the categorical variables or dummy encoding them. I think one-hot-encoding makes sense for regression, but I don't have space to have that many additional columns. In this case, I prefer dummy encoding and I know how to do it. However, I don't know how to use the dummy numbers when doing linear regression using scikit learn.

• I think one-hot-encoding or dummy encoding they are the synonyms. Dummy is an old established term in statistics. I suppose one-hot-encoding is coming from computer science but I'm not sure. – ttnphns Dec 25 '16 at 22:43
• dummy and one hot are not the same ! Dummy creates n-1 variables and one hot creates n. – meh Jun 7 '17 at 16:33
• @aginensky, I don't agree with your terminology. It is strange that same coding should be termed differently depending on whether there is or is not a subsequent removal of one variable from the set. – ttnphns Mar 17 '19 at 2:22
• @ttnphns It's not my terminology. Look up dummy coding for lm . The point is that if you don't remove one variable, the design matrix will be singular. Have fun using one-hot encoding on a regression model (on the theory that it's the same as one dummy encoding). – meh Mar 18 '19 at 15:54