  This text discusses several popular explanatory methods that go beyond the
error measurements and plots traditionally used to assess machine learning
models. Some of the explanatory methods are accepted tools of the trade while
others are rigorously derived and backed by long-standing theory. The methods,
decision tree surrogate models, individual conditional expectation (ICE) plots,
local interpretable model-agnostic explanations (LIME), partial dependence
plots, and Shapley explanations, vary in terms of scope, fidelity, and suitable
application domain. Along with descriptions of these methods, this text
presents real-world usage recommendations supported by a use case and public,
in-depth software examples for reproducibility.
