specific feature engineering for specific algorithm 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.
In general, features are engineered so as to retain optimum relevant information present in the dataset with succinct representation and then features are adapted so that an algorithm can accept it as input.

Feature engineering generally involves methods like binning, PCA, etc. Adapting those features to pass to algorithm is another step which is a very small part of the feature engineering step. Adapting example: for an image, we may have to reshape the image like below so that x[0] points to the image

x = image.img_to_array(img)
normalise(x) // feature engineering
x = x.reshape((1,) + x.shape) // Adapting for algorithm


With this understanding, if features are generated once for a dataset, it may be used for any relevant algorithm with corresponding adaption.