Supervised learning methods come in a variety of flavors. While local averaging techniques such as nearest-neighbors or decision trees are often used with low-dimensional inputs where they can adapt to any potentially non-linear relationship between inputs and outputs, methods based on empirical risk minimization are the most commonly used in high-dimensional settings. Their principle is…