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I have 10 million categorical variables (each variable has 3 categories). What is the best way to encode these 10 million variables to train a deep learning model on them? (If I use one hot encoding, then I will end up having 30 million variables. Also, embedding layer with one output makes no sense (it is similar to integer encoding and there is no order between these categories) and embedding layer with two outputs does not make that much difference. Usually, we use embedding layer when number of categories is a lot). Please give me your opinion.

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  • $\begingroup$ We probably can't answer "best", since that is likely to depend intimately on the details of your specific situation, and it might not be knowable without trying different approaches empirically. $\endgroup$ – D.W. Oct 17 '20 at 4:07

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