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Balancing scale and interpretability in analytical applications with sklearn and ensembling methods

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We present a machine learning framework using ensemble learning to combine models developed by multiple analysts for distinct subsets of a large feature space. We apply the framework to retail sales data, but its design can accommodate other types of target variables. This is a novel application of ensemble learning since it addresses both analytical and organizational challenges.

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