I'm familiar with the common methods:
- Label Encoding:
{A, B, C}
->[0, 1, 2]
- One-Hot Encoding:
{A, B, C}
->[[1,0,0][0,1,0][0,0,1]]
What other encoding methods exist and when would I need to use them over the two common methods?
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Sign up to join this communityI'm familiar with the common methods:
{A, B, C}
-> [0, 1, 2]
{A, B, C}
-> [[1,0,0][0,1,0][0,0,1]]
What other encoding methods exist and when would I need to use them over the two common methods?
Embeddings:
A --> [vector_A] B --> [vector_B] ...
They are frequently used in neural networks to represent categorical values that can have a lot of different discrete values, like text. The vectors are obtained either in an ad-hoc previous training step or during the training of the model itself.