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The two types of interactions are fundamentally different and complementary, which indicates that our predictions can substantially expand the knowledge about pairwise binary interactions.
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Therefore, finding an optimal set of variables by discarding the uninformative variables from the data can substantially improve the DPLS model by a decrease of the prediction error for test samples or/and a decrease of model complexity.
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