Parametric regression models became the dominant tool of quantitative sociology. This dominance is not without its challenges and many criticisms have been expressed, both statistically and epistemologically. Still, the development of data mining, and then of machine learning, has led to the emergence of methodological approaches that make it possible to overcome most of the limitations of parametric regression models, for the various types of use that are of interest to the social sciences. We argue that recursive partitioning in particular may be highly vauable for social sciences. Indeed, this approach has a number of technical advantages over parametric regression and, above all, it is consistent with a conception of social determinations in terms of configurations of interdependent factors (and not of additions of independent factors). In a second step, we review a range of tools for interpreting the results obtained from recursive partitioning algorithms. Together, they form a very complete toolbox for the social sciences and show that recursive partitioning is no longer a black box as soon as the appropriate interpretative tools are mobilized. Finally, we illustrate the methods presented using sociological examples from the world of cinema. In doing so, we will show that these methods make it possible to deal with the different types of problems that arise in the social sciences when parametric regressions are usually used, in this case the study of structure effects and the ranking of explanatory factors.