Unbiased Recursive Partitioning: A Conditional Inference Framework

By Torsten Hothorn, Kurt Hornik, Achim Zeileis. 2006

3,729 citations recorded by the source corpus.

Subfields: Food Science, Statistics and Probability

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Citations in the corpus

Forest Floor Visualizations of Random Forests (2016, methodology): “Although the RF algorithm by Breimann [3] has achieved the most journal citations, other later decision tree ensemble models/algorithms such as ExtraTrees [14], conditional inference forest [8], Aborist [21], Ranger [26] and sklearn.”
A review on longitudinal data analysis with random forest (2022, neutral): “Examples include GUIDE [32] and conditional inference [23] approaches.”
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? (2012, neutral): “been designed for unbiased variable selection (discussed below) [107].”
A random forest guided tour (2015, methodology): “Hothorn et al. (2006) propose a two-step procedure to correct this situation by first selecting the splitting variable and then the position of the cut along the chosen variable.”
Gaining insight with recursive partitioning of generalized linear models (2013, methodology): “Trees have become a widely researched method since their ﬁrst inception by Morgan and Sonquist (1968), see e.g., Breiman, Friedman, Olshen, and Stone (1984), Quinlan (1993), Hothorn, Hornik, and Zeileis (2006), Zhang and Singer (2010).”
Sifting and winnowing: Analysis of farmer field data for soybean in the US North-Central region (2018, methodology): “Nevertheless, this approach has been criticized for lack of concept of statistical significance (Mingers, 1987), data overfitting, and selection bias towards covariates with many levels and many missing observations (Hothorn et al., 2006).”
Understanding Random Forests: From Theory to Practice (2014, methodology): “Among all alternatives, conditional inference trees [Hothorn et al., 2006] and earlier methods [Quinlan, 1986; Wehenkel, 1998] make use of statistical tests for assessing the independence of Xj and Y at a pre-specified level of confidence α.”
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, background): “[100]), causal forests for formal statistical inference (see e.”
Approximate Gaussian Integration using Expectation Propagation (2011, methodology): “problem that can be well handled by quasi-random or lattice point numerical integration. Many important studies across many elds have been critically enabled by these and other algorithms (Joe, 1995; Hothorn et al., 2005; Boyle et al., 2005; Hickernell and Hong, 1999; Thiebaut and Jacqmin-Gadda, 200”
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? (2017, background): “Gini and information gain are known to be biased towards large-domain features in decision trees [8] and different approaches have explored alternatives to resolve this issue [20].”