Function Trees: Transparent Machine Learning
Jerome H. Friedman
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.