I am currently training a random forest. After transforming a categorical feature into dichotomous columns, should I drop the first level?
For example, I have three unique values in a featured named
nafor not available
Thus, I encoded
sex into three columns:
sex sex_m sex_f sex_na m 1 0 0 f 0 1 0 na 0 0 1
sex (obviously), but should I also drop one of the three encoded columns?
Dropping the base level is necessary when running a regression to avoid multicollinearity, but this is not a problem when running a random forest. So what is the most common approach?
For reference, each tree is being trained with a randomly selected set of 8 out of 63 features.