Fmeffects: An R Package for Forward Marginal Effects

MCML Authors

Christian Alexander Scholbeck

Dr.

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Giuseppe Casalicchio

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Abstract

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.

article

---

The R Journal

16.3. May. 2025.

---

Authors

H. Löwe • C. A. Scholbeck • C. Heumann • B. Bischl • G. Casalicchio

---

Links

DOI

---

Research Area

 A1 | Statistical Foundations & Explainability