I'm dealing with a low event rate problem (e.g. credit card fraud). I've balanced my data with SMOTE, and ran a neural net model (cross validated with recall as the measure).

However my precision (as can be expected) is very low.

I want to use the positive predictions (TP + FP) from my model as an input to another model - a subset of the original data. Creating a sort of "pipeline". Practically speaking, If a credit card company were to use my model, all the positive predictions will be handled by a human who would than decide whether or not it's a fraud, so why not automate?

Just like model stacking, but the stacked model gets a subset of the data and not the previous models predictions.

I'm not sure if that's OK. Any thoughts?


stacking, is exactly use the prediction of previous models. You can find the ensemble method in the following book:

Z.-H. Zhou. Ensemble Methods: Foundations and Algorithms, Boca Raton, FL: Chapman & Hall/CRC, 2012. (ISBN 978-1-439-830031)

I think you method, just use the TP and FP not very well, except the recall metrics is good enough.


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