María D. Guillén (Center of Operations Research, Miguel Hernandez University of Elche), Juan Aparicio (Center of Operations Research, Miguel Hernandez University of Elche) and Víctor J. España (Center of Operations Research, Miguel Hernandez University of Elche)
Abstract:
boostingDEA is a new package for R that includes functions to estimate production frontiers and make ideal output predictions in the Data Envelopment Analysis (DEA) context. The package implements both standard models from DEA and Free Disposal Hull (FDH) and, for the first time, incorporates boosting techniques. Boosting is a method used in machine learning that attempts to overcome the overfitting issue, typically sustained in standard methods, by training multiple models sequentially to improve the accuracy of the overall system. Moreover, the package includes code for estimating several technical efficiency measures using different models such as the input and output-oriented radial measures, the input and output-oriented Russell measures, the Directional Distance Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based Measure (SBM).