The output of a machine learning algorithm can usually be represented by one or more multivariate functions of its input variables. Knowing the global properties of such functions can help in understanding the system that produced the data as well as interpreting and explaining corresponding model predictions. A method is presented for representing a general multivariate function as a tree of simpler functions. This tree exposes the global internal structure of the function by uncovering and describing the combined joint influences of subsets of its input variables. Given the inputs and corresponding function values, a function tree is constructed that can be used to rapidly identify and compute all of the function's main and interaction effects up to high order. Interaction effects involving up to four variables are graphically visualized.

翻译：机器学习算法的输出通常可由一个或多个输入变量的多元函数表示。了解此类函数的全局特性有助于理解产生数据的系统，同时还能解释和说明相应的模型预测结果。本文提出一种将一般多元函数表示为简单函数树的方法。该树状结构通过揭示并描述输入变量子集的联合影响，展现函数的全局内部结构。基于输入变量及对应函数值，可构建函数树，用于快速识别并计算所有主效应及高达高阶的交互效应。涉及最多四个变量的交互效应可通过图形化方式可视化呈现。