Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

fmeffects: An R Package for Forward Marginal Effects (2310.02008v2)

Published 3 Oct 2023 in cs.LG, econ.EM, and stat.ML

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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (26)
  1. V. Arel-Bundock. marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests, 2023. URL https://CRAN.R-project.org/package=marginaleffects. R package version 0.11.1.
  2. T. Bartus. Estimation of marginal effects using margeff. The Stata Journal, 5(3):309 – 329, 2005.
  3. M. Borkovec and N. Madin. ggparty: ’ggplot’ Visualizations for the ’partykit’ Package, 2019. URL https://CRAN.R-project.org/package=ggparty. R package version 1.0.0.
  4. W. Chang. R6: Encapsulated Classes with Reference Semantics, 2021. URL https://CRAN.R-project.org/package=R6. R package version 2.5.1.
  5. H. Fanaee-T. Bike Sharing Dataset. UCI Machine Learning Repository, 2013. URL https://doi.org/10.24432/C5W894.
  6. J. H. Friedman. Greedy function approximation: A gradient boosting machine. Ann. Statist., 29(5):1189–1232, 10 2001. URL https://doi.org/10.1214/aos/1013203451.
  7. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley Professional, 1 edition, 1994. ISBN 0201633612.
  8. Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical Statistics, 24(1):44–65, 2015. URL https://doi.org/10.1080/10618600.2014.907095.
  9. W. Greene. Econometric Analysis. Pearson International Edition. Pearson Education Limited, 2012. ISBN 9780273753568.
  10. T. Hothorn and A. Zeileis. partykit: A modular toolkit for recursive partytioning in R. Journal of Machine Learning Research, 16(118):3905–3909, 2015.
  11. M. Kuhn. Building predictive models in r using the caret package. Journal of Statistical Software, 28(5):1–26, 2008. URL https://doi.org/10.18637/jss.v028.i05.
  12. M. Kuhn and D. Vaughan. parsnip: A Common API to Modeling and Analysis Functions, 2023. URL https://CRAN.R-project.org/package=parsnip. R package version 1.1.1.
  13. M. Kuhn and H. Wickham. Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles, 2020. URL https://www.tidymodels.org.
  14. mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software, dec 2019. URL https://joss.theoj.org/papers/10.21105/joss.01903.
  15. T. J. Leeper. margins: Marginal effects for model objects, 2018. URL https://CRAN.R-project.org/package=margins. R package version 0.3.23.
  16. D. Lüdecke. ggeffects: Tidy data frames of marginal effects from regression models. Journal of Open Source Software, 3(26):772, 2018. URL https://doi.org/10.21105/joss.00772.
  17. A general framework for comparing predictions and marginal effects across models. Sociological Methodology, 49(1):152–189, 2019. URL https://doi.org/10.1177/0081175019852763.
  18. C. Molnar. Interpretable Machine Learning. 2nd edition, 2022. URL https://christophm.github.io/interpretable-ml-book.
  19. iml: An R package for interpretable machine learning. JOSS, 3(26):786, 2018.
  20. Humid heat waves at different warming levels. Scientific Reports, 7(1):7477, Aug 2017. ISSN 2045-2322. URL https://doi.org/10.1038/s41598-017-07536-7.
  21. Sampling, intervention, prediction, aggregation: A generalized framework for model-agnostic interpretations. In P. Cellier and K. Driessens, editors, Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1167, Cham, 2020. Springer. ISBN 978-3-030-43823-4. URL https://doi.org/10.1007/978-3-030-43823-4_18.
  22. Marginal effects for non-linear prediction functions. arXiv e-prints, 2022. arXiv:2201.08837.
  23. StataCorp. Stata: Release 18. College Station, TX: StataCorp LLC., 2023.
  24. T. Therneau and B. Atkinson. rpart: Recursive partitioning and regression trees, 2019. URL https://CRAN.R-project.org/package=rpart. R package version 4.1-15.
  25. R. Williams. Using the margins command to estimate and interpret adjusted predictions and marginal effects. Stata Journal, 12(2):308–331(24), 2012.
  26. M. N. Wright and A. Ziegler. ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software, 77(1), 2017. ISSN 1548-7660. URL http://dx.doi.org/10.18637/jss.v077.i01.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now