mlsanityData to 7 Oct 2026

Forest Guided Smoothing

arXiv 2103.05092 ↗First posted 8 Mar 2021stat.ML cs.LG stat.MEcited by 3

Not on the map. Why?

Abstract (arXiv)

We use the output of a random forest to define a family of local smoothers with spatially adaptive bandwidth matrices. The smoother inherits the flexibility of the original forest but, since it is a simple, linear smoother, it is very interpretable and it can be used for tasks that would be intractable for the original forest. This includes bias correction, confidence intervals, assessing variable importance and methods for exploring the structure of the forest. We illustrate the method on some synthetic examples and on data related to Covid-19.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 8 Mar 2021.

Builds on

7 of 21 references.

References · 7

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  3. 2016Generalized Random Forestscited by 309
  4. 2018Model Agnostic Supervised Local Explanationscited by 83
  5. 2013Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknifecited by 43
  6. 2018Local Linear Forestscited by 19
  7. 2019Asymptotic Distributions and Rates of Convergence for Random Forests via Generalized U-statisticscited by 18

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1.

3 papers.

  1. 2024A survey and taxonomy of methods interpreting random forest modelscited by 2
  2. 2021WildWood: a new Random Forest algorithmcited by 0
  3. 2025Distributional Random Forests for Complex Survey Designscited by 0