computing lift in customer mail targeting; data audit operator?

dan_agapedan_agape Member Posts: 106 Maven
edited November 2018 in Help
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!



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    landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    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.

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