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Training Distribution Strategies for Optimizing Neural Network Classification Models
Abstract
Neural networks have been repeatedly shown to outperform traditional statistical modeling techniques for both discriminant analysis and forecasting. While questions regarding the effects of architecture, input variable selection, learning algorithm, and size of training sets on the neural network model’s performance have been addressed, little attention has been focused on distribution effects of training and out-of-sample populations on neural network performance. This article examines the effect of changing the population distribution within training sets, in particular for a credit risk assessment problem.
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