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A vital problem in solving classification or regression problem is to applyfeature engineering and variable selection on data before fed into models.Oneof a most popular feature engineering method is to discretisize continousvariable with some cutting points,which is refered to as bining processing.Goodcutting points are important for improving model's ability, because wonderfulbining may ignore some noisy variance in continous variable range and keepuseful leveled information with good ordered encodings.However, to our bestknowledge a majority of cutting point selection is done via researchers domainknownledge or some naive methods like equal-width cutting or equal-frequencycutting.In this paper we propose an end-to-end supervised cutting pointselection method based on group and fused lasso along with the automaticallyvariable selection effect.We name our method \textbf{ABM}(automatic biningmachine). We firstly cut each variable range into fine grid bins and trainmodel with our group and group fused lasso regularization on each successivebins.It is a method that integrates feature engineering,variable selection andmodel training simultanously.And one more inspiring thing is that the method isflexible such that it can be taken into a bunch of loss function based modelincluding deep neural networks.We have also implemented the method in R andopen the source code to other researchers.A Python version will also meet thecommunity in days.

ABM: an automatic supervised feature engineering method for loss based models based on group and fused lasso