Holger Löwe, Christian A. Scholbeck, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
arXiv 3 Oct 2023 · Machine Learning · publishedThe R Journal (2025)
arXiv:2310.02008 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2024) Marginal effects for non-linear prediction functions self | 1.000 | 13 | 5 | 100% |
| 2 | R. Williams (2012) Using the margins command to estimate and interpret adjusted predictions and marginal effects | 0.843 | 3 | 3 | 100% |
| 3 | C. A. Scholbeck, C. Molnar, C. Heumann, B. Bischl, and G. Casalicchio (2020) Sampling, intervention, prediction, aggregation: A generalized framework for model-agnostic interpretations self | 0.737 | 3 | 2 | 100% |
| 4 | D. W. Apley and J. Zhu (2020) Visualizing the effects of predictor variables in black box supervised learning models | 0.644 | 2 | 2 | 100% |
| 5 | T. Bartus (2005) Estimation of marginal effects using margeff | 0.644 | 2 | 2 | 100% |
| 6 | L. Breiman (2001) Statistical modeling: The two cultures (with comments and a rejoinder by the author) | 0.644 | 2 | 2 | 100% |
| 7 | M. Britton (1904) Vine: Visualizing statistical interactions in black box models | 0.644 | 2 | 2 | 100% |
| 8 | W. Greene (2019) Econometric Analysis | 0.644 | 2 | 2 | 100% |
| 9 | J. Herbinger, B. Bischl, and G. Casalicchio (2022) Repid: Regional effect plots with implicit interaction detection | 0.644 | 2 | 2 | 100% |
| 10 | C. Molnar (2022) Interpretable Machine Learning | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 51 scored citations.