I have trained two classifiers namely a logistic regression and a decision tree on a data set. When evaluating both models on the testing data set, the decision tree has a ROC_AUC = 0.62 but the logistic regression has ROC_AUC = 0.91. However both models have similar gain chart and the lift for the first decile is almost the same and is 5.7 and this is almost the highest lift I can get considering my response rate (~17%). How come that is possible and what it means?

I have trained two classifiers namely a logistic regression and a decision tree on a data set. When evaluating both models on the testing data set, the decision tree has a ROC AUC = 0.62 but the logistic regression has ROC AUC = 0.91. However both models have similar gain chart and the lift for the first decile is almost the same and is 5.7 and this is almost the highest lift I can get considering my response rate (~17%). How come that is possible and what it means?

I have trained two classifiers namely a logistic regression and a decision tree on a data set. When evaluating both models on the testing data set, the decision tree has a ROC_AUC = 0.62 but the logistic regression has ROC_AUC = 0.91. However both models have similar gain chart and the lift for the first decile is almost the same and is 6.2. How come that is possible and what it means?

I have trained two classifiers namely a logistic regression and a decision tree on a data set. When evaluating both models on the testing data set, the decision tree has a ROC_AUC = 0.62 but the logistic regression has ROC_AUC = 0.91. However both models have similar gain chart and the lift for the first decile is almost the same and is 5.7 and this is almost the highest lift I can get considering my response rate (~17%). How come that is possible and what it means?