featuretools dfs vs categorical_encoding

Question

When I want to add categorical_encoding I can do it in two different ways :

1. With dfs with setting categorical feature as relationship and getting mean/std/skew statistics . In this case categorical feature and value/s in same dataframe
2. With categorical_encoding sub-library and fit_transform

I see the only difference that in second case I have wider range of parameters , i.e. setting method='leave_one_out' that can be more accurate than using regular mean in case of dfs

Am I right ? If categorical_encoding uses parallel processing ?

Answer 1

You can do the categorical encoding with DFS and also stack additional primitives to create new features. The library for categorical encoding does not use parallel processing, but does provide a wider range of encoders.