ML requires manual feature extraction whereas DL doesn't necessarily require feature engineering, since recent advanced models like transformers learn necessary features automatically during training - even the sequence of data.
However, I'm still not sure if it means that the model can automatically pick up the features that help understand the data pattern without any preprocessing whatsoever.
Difference is something very simple yet important when you do any sort of feature engineering. For example you have to take difference of price to get the price change, and you have to take difference of timestamps to get the duration of some logs.
This is very simple:
However, can ML models and DL models automatically "use" this feature without explicitly given?
Say I have some columns like
Is it absolutely pointless to add
There are many ML and DL models but I want to know if there are models that are robust to these feature selections so that the model renders feature engineering useless.