Cingestol

An important problem in statistical modeling (regression or classification) is to build models that generalize well.The authors propose a method to combine two or more models each obtained using cross-validation (CV), to generate a model that is better at generalization compared to the original ones.Such combinations are especially useful when cross-validation experiments indicate many possible solutionsThe basic idea of the procedure described comes from actively making use of the predictions of cross-validation experiments, not just for calculating the CV error.The method is applied and is shown to perform well when building quant. structure-activity relationships (QSARs) for steroids using neural networks.The proposed method has a much broader applicability than QSAR or neural networks.