Well I have check all my models I have done decision tree , linear regression , forest , neural network, maximum regression with the parameter misclassification error minimization I check the misclassification error table , I don't have any improvement with the different model :'( One is slightly better with at the level of the classification table concerning my problem, progress of 10 clients. I was just thinking they were maybe other parameter, statistical wich allow to invers somehow the analysis. My problem is I am trying to identify client who buy during one period I identify only 10% of the client who buy something so I have wrongly identied 90% of the clients who buy. This 90% who buy effectively are predicted as not buyer false negative 100% of my customer who buy is egual to 1700 more or less. My whole sample is 15 000 clients. So I have 13 300 clients in this period who doesn't buy anything. But I would like to revert this analysis somehow 😛 I think that the problem is maybe a lack of linear correlation between my class imputs , can I do anything to improve this ? Have you one other explaination about why, this phenomenom appears thank you for your answer !!!
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