Dear all modelling experts, I have built an attrition model by using logistic regression algorithm. Considering r two months operational gap to see the probability of a customer to be churned. In model development process, I'm getting a very good model accuracy with ROC(.94) and Gini(.75).However, while I'm looking at the probability of events category - by looking at P_1.the scores are showing a bit low though the events occurred accurately. Could anyone suggest how could I adjust the probability scores i.e. if I target top 3 deciles population then I have to consider a customer who has a chance to attrite 30% at decile 1(top 10% population) and in actual world that customer is also churning , in that case, how could I reweight that customer as 90% at decile 1. I have tried to reweighted by AIC or -2logL but still the scores are same. It would be really great to have an expert suggestions. please find below the average scores distribution from model scores: Row Labels Average of SCORE_1 Actually closed # RISK Customer Grand 0 0.858566898 485 16531 0.029338818 1 0.602700652 345 16322 0.021137116 2 0.459997375 321 16343 0.019641437 3 0.25116008 401 16661 0.024068183 4 0.13434664 523 16014 0.032658923 5 0.070197285 322 18175 0.017716644 6 0.039646414 429 17278 0.024829263 7 0.03082105 356 13737 0.025915411 8 0.019729811 307 17477 0.017565944 9 0.009666523 278 15341 0.018121374 Regards, Mou
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