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about the wide feature and deep feature #3
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Hi @Usernamezhx ok, let me explain. If you go here: you will read the following:
The functions in
what will happen is that while Regarding to the other column-type inputs, if you go here: you will read the following:
The wide columns are normally one-hot encoded and then pass through the model. However, there might be some columns that are already one hot encoded, and I call them And regarding to your last question: "how to select the element to constitute interaction feature?" The answer is that you have to experiment, there is no rule for that. For example, if you have a couple of features and you think that including their relation might add useful information, then is probably useful if you "cross them". For example, directly from the tensorflow tutorials: "...If you have a feature 'favorite_sport' and a feature 'home_city' and you're trying to predict whether a person likes to wear red, your linear model won't be able to learn that baseball fans from St. Louis especially like to wear red..." Let me know if this helps |
Thank you very much for your patiently reply. |
first of all. thanks for your work. I am a little confuse about the split of the wide and deep feature. embeddings_cols, continuous_cols, standardize_cols, wide_cols, crossed_cols, already_dummies. embeddings_cols is the category feature. continuous_cols is continuous feature. what's the different between standardize_cols and continuous_cols? the wide_cols feature is similar to the category feature. crossed_cols I know you want to get the interaction feature.how to select the element to constitute interaction feature? thanks in advance.
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