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Dummy Coding

Specifications

  • Good for: Low number of values

Description

The dummy coding scheme is similar to the one-hot encoding scheme, except in the case of the dummy coding scheme, when applied on a categorical feature with m distinct labels, we get m - 1 binary features. Thus each value of the categorical variable gets converted into a vector of size m - 1. The extra feature is completely disregarded and thus if the category values range from {0, 1, …, m-1} the 0th or the m - 1th feature column is dropped and corresponding category values are usually represented by a vector of all zeros (0).

Example

gen_dummy_features = pd.get_dummies(poke_df['Generation'], drop_first=True)

pd.concat([poke_df[['Name', 'Generation']], gen_dummy_features], axis=1).iloc[4:10]