fmeffects: An R Package for Forward Marginal Effects
This package addresses the need for interpretable explanations in machine learning models, particularly for complex non-linear cases, but it is incremental as it focuses on software implementation rather than new theoretical insights.
The authors introduced the R package fmeffects, which implements forward marginal effects as a model-agnostic interpretation method for non-linear and non-parametric prediction models, providing the first software implementation of this theory.
Forward marginal effects have recently been introduced as a versatile and effective model-agnostic interpretation method particularly suited for non-linear and non-parametric prediction models. They provide comprehensible model explanations of the form: if we change feature values by a pre-specified step size, what is the change in the predicted outcome? We present the R package fmeffects, the first software implementation of the theory surrounding forward marginal effects. The relevant theoretical background, package functionality and handling, as well as the software design and options for future extensions are discussed in this paper.