mlsanityData to 7 Oct 2026

Narrowing the Gap: Random Forests In Theory and In Practice

arXiv 1310.1415 ↗First posted 4 Oct 2013stat.ML cs.LGcited by 17

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Abstract (arXiv)

Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of random regression forests and prove that our algorithm is consistent. We also provide an empirical evaluation, comparing our algorithm and other theoretically tractable random forest models to the random forest algorithm used in practice. Our experiments provide insight into the relative importance of different simplifications that theoreticians have made to obtain tractable models for analysis.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Oct 2013.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 4; 2024: 1; 2025: 3; 2026: 1.

17 papers.

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  1. 2022Adversarial random forests for density estimation and generative modelingcited by 44
  2. 2021Large Scale Prediction with Decision Treescited by 28
  3. 2022Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guaranteecited by 9
  4. 2021Muddling Label Regularization: Deep Learning for Tabular Datasetscited by 5
  5. 2021There is no Double-Descent in Random Forestscited by 4
  6. 2023Stability of Random Forests and Coverage of Random-Forest Prediction Intervalscited by 4
  7. 2021Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinementcited by 3
  8. 2021Label Ranking through Nonparametric Regressioncited by 3
  9. 2025Autoencoding Random Forestscited by 3
  10. 2023Targeting relative risk heterogeneity with causal forestscited by 1
  11. 2025Multivariate Uncertainty Quantification with Tomographic Quantile Forestscited by 1
  12. 2022Data-driven multinomial random forest: A new random forest variant with strong consistencycited by 0
5 more
  1. 2023Data-driven multinomial random forestcited by 0
  2. 2023Generalized Random Forests using Fixed-Point Treescited by 0
  3. 2024The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forestcited by 0
  4. 2025Targeted tuning of random forests for quantile estimation and prediction intervalscited by 0
  5. 2026Covariance-Driven Regression Trees: Reducing Overfitting in CARTcited by 0