The advent of the Big Data era has given birth to a variety of new architectures aiming at applications with increased scalability, robustness and fault tolerance. At the same time these architectures have complicated application structure, leading to an exponential growth of their configuration space and increased difficulty in predicting their performance. In this work, we describe a novel, automated profiling methodology that makes no assumptions on application structure. Our approach utilizes oblique Decision Trees in order to recursively partition an application's configuration space in disjoint regions, choose a set of representative samples from each subregion according to a defined policy and return a model for the entire space as a composition of linear models over each subregion. An extensive evaluation over real-life applications and synthetic performance functions showcases that our scheme outperforms other state-of-the-art profiling methodologies. It particularly excels at reflecting abnormalities and discontinuities of the performance function, as well as identifying the parameters with the highest impact on the application's behavior.