This research highlights the extent to which fiscal policies, along with government effectiveness, trade, economic growth, and education, influence the entrepreneurial environment, starting from the assumption that the effectiveness of public policies and the public administration support for entrepreneurship has proven to be a trigger for development and a means of reducing social inequalities and increasing well-being. Our analysis is based on a panel threshold regression model, which returns parameters for the predictors, in which there is a changing point in fiscal policy as the threshold variable on entrepreneurship activity. The database consisted of annual data from 2002-2019, referring to the European Union’s member states, presented visually based on the data mapping process to evidence the hierarchy of the EU countries for each of the analysed phenomena. Our results evidenced tax policy, trade, and government effectiveness as significant influential factors in stimulating entrepreneurship in EU countries. Although GDP growth and education positively influenced entrepreneurial activity, the statistical tests did not confirm it. Therefore, Governments can encourage entrepreneurial opportunities through harmonised tax legislation with EU regulations and a lighter regulatory burden and policies that foster competition, lower taxes, increase transparency and provide open access for all businesses.

In machine learning and its risk-sensitive applications (e.g. medicine, engineering, business), the reliability estimates for individual predictions provide more information about the individual prediction error (the difference between the true label and regression prediction) than the average accuracy of predictive model (e.g. relative mean squared error). Furthermore, they enable the users to distinguish between more and less reliable predictions. The empirical evaluations of the existing individual reliability estimates revealed that the successful estimates’ performance depends on the used regression model and on the particular problem domain. In the current paper, we focus on that problem as such and propose and empirically evaluate two approaches for automatic selection of the most appropriate estimate for a given domain and regression model: the internal cross-validation approach and the meta-learning approach. The testing results of both approaches demonstrated an advantage in the performance of dynamically chosen reliability estimates to the performance of the individual reliability estimates. The best results were achieved using the internal cross-validation procedure, where reliability estimates significantly positively correlated with the prediction error in 73% of experiments. In addition, the preliminary testing of the proposed methodology on a medical domain demonstrated the potential for its usage in practice.