document: On the use of neural network ensembles in QSAR and QSPR Despite their growing popularity among neural network practitioners , ensemble methods have not been widely adopted in structure-activity and structure-property correlation . Neural networks are inherently unstable , in that small changes in the training set and/or training parameters can lead to large changes in their generalization performance . Recent research has shown that by capitalizing on the diversity of the individual models , ensemble techniques can minimize uncertainty and produce more stable and accurate predictors . In this work , we present a critical assessment of the most common ensemble technique known as bootstrap aggregation , or bagging , as applied to QSAR and QSPR . Although aggregation does offer definitive advantages , we demonstrate that bagging may not be the best possible choice and that simpler techniques such as retraining with the full sample can often produce superior results . These findings are rationalized using Krogh and Vedelsby 's -LRB- 1995 -RRB- decomposition of the generalization error into a term that measures the average generalization performance of the individual networks and a term that measures the diversity among them . For networks that are designed to resist over-fitting , the benefits of aggregation are clear but not overwhelming
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