"..., but in my experience it's much more valuable to try and understand your data, and the process that generates it, and then build a model that tries to refl

"..., but in my experience it's much more valuable to try and understand your data, and the process that generates it, and then build a model that tries to reflect that data generation process." There are tasks where I feel happy to accept a black box model; image recognition, speech transcription. In these tasks understanding the model is really important for further research, but it seems to me to be ok to say "look it just works". When it comes to data mining or task like predicting customer responses to offers I believe that arbitrary models that have high predictive power are dangerous. This is because you can't know when changes in your domain (like a new competitor or a shift in demographics) are liable to derail it, and experience shows that this can lead to really bad decisions from something that people have come to trust.