Days ago,One AI financial service provider offered us a lesson and mentioned that you are supposed to perform specific feature engineering according to the specific algorithm you are using.For example,when using logistics regression,fitting more features（uncorrelated）like binning the continuous variable into discrete ones are often suggested.Because logistics regression is a simple algorithm and we try to raise dimension in the way that samples will be separated better.
I searched a lot(maybe not yet),most of materials are "why/what feature engineering important","scaling/standardization/binning continuous variable","dealing with null value" or some theoretical comments with no discrete manipulation.
why and how the specific feature engineering should work on specific algorithm.Or any advice on this saying,is it right or wrong?what do you think.any comments are appreciated.
（I am not good at English,sorry about that if I am not clear enough）
I am not looking forward a detailed answer,some deep thinking about this part is good.