@Federico_Ottomano


If you train the parameters jointly along with the overall model, your embeddings will reflect the patterns that help distinguish the input based on the target


If you do it separately ( i am guessing autoencoder), then the embeddings will reflect the input features only


You can try a third option
Have a autoencoder setup for the input data and use the encoder output as an input where you concatenate it along with the other features and have the prediction output. So your model will jointly train the autoencoder and the classifier