Exploring the Impact of BudgetPrune on Apache Spark Random Forest Performance
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2017-06-29
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en
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This thesis explores the impact of a previously proposed pruning algorithm for random forest ensembles called BudgetPrune. BudgetPrune
tries to optimize the tradeoff between prediction accuracy and feature
acquisition cost, allowing for accurate prediction in resource-constrained
environments. Using Apache Spark ML's random forest model as a baseline, the influence of the pruning step on prediction accuracy and cost is
examined.
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Faculteit der Sociale Wetenschappen