This paper aims at identifying relevant indicators for TFP growth in EU countries during the recovery phase following the 2008/09 economic crisis. We proceed in three steps: First, we estimate TFP growth by means of Stochastic Frontier Analysis (SFA). Second, we perform a TFP growth decomposition in order to get measures for changes in technical progress (CTP), technical efficiency (CTE), scale efficiency (CSC) and allocative efficiency (CAE). And third, we use BART - a non-parametric Bayesian technique from the realm of statistical learning - in order to identify relevant predictors of TFP and its components from the Global Competitiveness Reports. We find that only a few indicators prove to be stable predictors. In particular, indicators that characterize technological readiness, such as broadband internet access, are outstandingly important in order to push technical progress while issues that describe innovation seem only to speed up CTP in higher-income economies. The results presented in this paper can be guidelines to policymakers as they identify areas in which further action could be taken in order to increase economic growth. Concerning the bigger picture, it becomes obvious that advanced machine learning techniques might not be able to replace sound economic theory but they help separating the wheat from the chaff when it comes to selecting the most relevant indicators of economic competitiveness.