computing lift in customer mail targeting; data audit operator?
dan_agape
New Altair Community Member
Hi there,
I would appreciate any answer for the questions below.
Is the Lift included in some performance evaluation operator in RM? More concretely, can a process be built in RM that would select the best customers to be mailed offers? This would be based on building a number of classification models and selecting the best one that shows the highest Lift (not accuracy).
Something else: how can usual statistics be obtained on the attributes? Probabilistic distributions for the nominal attributes would be useful too. More generally, is there a data audit operator in RM?
Many thanks for your input!
Dan
I would appreciate any answer for the questions below.
Is the Lift included in some performance evaluation operator in RM? More concretely, can a process be built in RM that would select the best customers to be mailed offers? This would be based on building a number of classification models and selecting the best one that shows the highest Lift (not accuracy).
Something else: how can usual statistics be obtained on the attributes? Probabilistic distributions for the nominal attributes would be useful too. More generally, is there a data audit operator in RM?
Many thanks for your input!
Dan
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Answers
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Hi Dan,
of course such a process can be built. At least it's always possible to calculate whateever you want from the confidences of a learner. And if you have costs at your hand, you could automatically change classification over the confidences so that you will optimize the outcome. See the sample process 14_CostSensitiveLearningAndROC in 01_Learner directory of the samples repository for details.
You could use the NaiveBayes learner for drawing some statitics like counts / mean and standard deviation. Just take a look at it's model.
Greetings,
Sebastian0
