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(Of course, there are linear, non-monotonic machine-learned response functions that can, for instance, be created by the multi-variate adaptive regression splines approach.These functions are not highlighted here because they tend to be less accurate predictors than purely nonlinear, non-monotonic functions, while also lacking the high interpretability of their monotonic counterparts.) Nonlinear, non-monotonic functions: Most machine learning algorithms create nonlinear, non-monotonic response functions.While understanding and trusting models and results is a general requirement for good (data) science, model interpretability is a serious legal mandate in the regulated verticals of banking, insurance, and other industries.Business analysts, doctors, and industry researchers simply must understand and trust their models and modeling results.Because small sections of the conditional distribution are more likely to be linear, monotonic, or otherwise well-behaved, local explanations can be more accurate than global explanations.Machine learning algorithms and the functions they create during training are sophisticated, intricate, and opaque.Nonlinear, monotonic functions: Although most machine learned response functions are nonlinear, some can be constrained to be monotonic with respect to any given independent variable.While there is no single coefficient that represents the change in the response function induced by a change in a single independent variable, nonlinear and monotonic functions do always change in one direction as a single input variable changes.

(This relationship might also be referred to as the conditional distribution of the dependent variables, given the values of the independent variables.) These functions can then make very specific predictions about the values of dependent variables for new data—whether a donor will give to a charity, an infant in a NICU will develop sepsis, a customer will respond to an ad, etc.

In this article, I present several approaches beyond the usual error measures and assessment plots for visualizing data and interpreting machine learning models and results.

Users are encouraged to mix and match these techniques to best fit their own needs.

The techniques presented here go beyond the standard practices to engender greater understanding and trust.

These techniques enhance understanding by providing specific insights into the mechanisms of the algorithms and the functions they create, or by providing detailed information about the answers they provide.

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For this reason, linear models were the go-to applied predictive modeling tool for decades, even though it usually meant giving up a couple points on the accuracy scale.

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