We introduce a new method for the estimation of production technologies in a multi-input multi-output context, based on OneClass Support Vector Machines with piecewise linear transformation mapping. We compare via a finite-sample simulation study the new technique with Data Envelopment Analysis (DEA) to estimate technical efficiency. The criteria adopted for measuring the performance of the estimators are bias and mean squared error. The simulations reveal that the approach based on machine learning seems to provide better results than DEA in our finite-sample scenarios. We also show how to adapt several well-known technical efficiency measures to the introduced estimator. Finally, we compare the new technique with respect to DEA via its application to an empirical database of USA schools from the Programme for International Student Assessment, where we obtain statistically significant differences in the efficiency scores determined through the Slacks-Based Measure.