Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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This 2004 KDD Cup presents a perfect case where the usual neural network objective functions do not apply. While the contest problem consisted of 4 different entries with 4 different objective functions, this paper will focus on the solution optimizing GRMSE (Grouped Root Mean Squared Error). It will be shown that the more typical objective functions (including RMSE) cannot be as effective at meeting this criteria. While this objective function may be specific to this problem, and the reader may never see this exact function again in his/her lifetime, the idea behind this paper is applicable in many situations. Too often neural networks are used to minimize SSE (sum of the squares of the errors) or Cross Entropy, when the true measure of success for the model may require a small coding change to the Neural Network objective function. It is shown in this paper that a few small coding changes can make a big difference on a model's performance.