In this paper, we address the problem of full option ranking in hierarchical qualitative evaluation models. Qualitative evaluation models perform partial ranking of options. The main challenge for full ranking of options with hierarchical models is finding a suitable aggregation function. A common way to define an aggregation is to use some kind of regression. Current approaches that use linear regression often fail to provide a full ranking of options in non-linear cases. Therefore we propose different methods for linear and copula-based regression functions. We evaluate two real data-mining workflows and two of its modifications, and show that full ranking of options is achieved when employing copula-based regression using Frank copula.