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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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  • $\begingroup$ There is also Target encoding, but embedding sounds better\smarter. Which task do you want to solve using (un)supervised setup? $\endgroup$
    – Mario
    Dec 15, 2022 at 1:53

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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.

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  • $\begingroup$ My understanding of embeddings is that they are lower dimensional transformations of higher-dimensional encodings e.g. a truncated PCA on a one-hot-encoded matrix. Is that right? $\endgroup$
    – njp
    Dec 13, 2022 at 22:41
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    $\begingroup$ Well, they don't necessarily need to be of lower dimensionality, they just are learned representations in a continuous representation space. Also, I don't know if the one-hot + PCA example is sound; normally, examples are trained so that "close" discrete values are close in the embedded space, for instance, skipgrams in text reflect the word co-occurrence statistics. $\endgroup$
    – noe
    Dec 14, 2022 at 8:47

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