Dropping binary features with OneHotEncoder refers to the process of excluding one of the binary features generated by OneHotEncoder for each categorical variable. This is done to avoid the “dummy variable trap”, which is a situation where the model has perfect multicollinearity between two or more variables.
Example #1
Here’s an example of how to use OneHotEncoder with binary features and drop the first feature for each categorical variable:
As you can see, the first binary feature for each categorical variable has been dropped, resulting in a matrix with one less column for each categorical variable.
Example #2
Here’s another example of using OneHotEncoder to drop binary features:
By setting the drop parameter to “if_binary”, OneHotEncoder automatically drops any binary feature for each categorical variable.