Suppose you have data with a bunch of predictors, and some of these predictors are proportions that add up to one. An example would be data like the following
gender perc_shop perc_game perc_stud age
M .23 .71 .06 31
F .47 0 .53 19
F .05 .31 .64 29
The variables in columns 2-4 all add up to one, so in logistic regression it would be necessary to remove one as a baseline variable. However, in building a classification model using machine learning methods (i.e. decision trees, random forests, svm, etc.) would it be necessary to remove one of the variables?
