one approach I have tried when preprocessing high cardinality categorical features (for example, US City) is to do a value count of all the values in the data, then take the top x most frequently occurring values (with x depending on the frequency distribution), and then create a binary flag feature like 'is_in_top_x_us_cities'. Or creating one-hot features for each of the top x cities.

Can someone explain the relative disadvantages to this approach as opposed to using something like Weight of Evidence binning?


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