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Oh i see. Thats a good idea. You can try that and see how the model would perform. The only issue is that the embedding vectors are not true values. For example, the input categorical data represents ground truth. But all your parameters in the model represent a function which is meant to be close to the actual mapping function, but almost never the same. So the information represented by embedding vectors is not the same as the actual information that your categorical input represents. This would affect your accuracy a little. However, What you’re trying to do would work, and maybe even give good results. But the right approach (theoretically) would be to use the ground truth data as your input
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I’m still a little confused. From what I understand, you want to feed all continuous variables and the embedding vectors into the RF model. Am i right? In that case, you would want to simply pick up the embedding vector parameters from the NN model, and feed it to the RF.
The targets are always class categories. Eg - Positive or Negative ; Cat or Dog, etc. So you would have to train any model with the output being the classes that you’re trying to classify. -
Ah okay, I understand. You cant do anything. Your model will give some output, but it will definitely be a wrong prediction. Your model can only work well on the type of data that you’ve trained your model on. But still, is there anything specific in your mind?
Cheers