Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include: - An efficient formulation of the decision tree algorithm, tailored for Random Forests; - Cythonization of the tree induction algorithm; - CPU cache optimizations, through low-level organization of data into contiguous memory blocks; - Efficient multi-threading through GIL-free routines; - A dedicated sorting procedure, taking into account the properties of data; - Shared pre-computations whenever critical. Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.