By supervised learning with p predictors, we frequently obtain a prediction function of the form $y;=;f(x_1,...,x_p)$. When $p;{geq};3$, it is not easy to understand the inner structure of f, except for the case the function is formulated as additive. In this study, we propose to use p simple graphs for visual understanding of complex prediction functions produced by several supervised learning engines such as LOESS, neural networks, support vector machines and random forests.