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Machine Learning (ML) has been successfully applied to a wide range ofdomains and applications. One of the techniques behind most of these successfulapplications is Ensemble Learning (EL), the field of ML that gave birth tomethods such as Random Forests or Boosting. The complexity of applying thesetechniques together with the market scarcity on ML experts, has created theneed for systems that enable a fast and easy drop-in replacement for MLlibraries. Automated machine learning (autoML) is the field of ML that attemptsto answers these needs. Typically, these systems rely on optimizationtechniques such as bayesian optimization to lead the search for the best model.Our approach differs from these systems by making use of the most recentadvances on metalearning and a learning to rank approach to learn frommetadata. We propose autoBagging, an autoML system that automatically ranks 63bagging workflows by exploiting past performance and dataset characterization.Results on 140 classification datasets from the OpenML platform show thatautoBagging can yield better performance than the Average Rank method andachieve results that are not statistically different from an ideal model thatsystematically selects the best workflow for each dataset. For the purpose ofreproducibility and generalizability, autoBagging is publicly available as an Rpackage on CRAN.

autoBagging: Learning to Rank Bagging Workflows with Metalearning