Classification and regression trees

By Leo Breiman. 1983

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In Statistical Learning Theory

Citations in the corpus

Analysis of a Random Forests Model (2010, positive): “[13]) to maximum size, without pruning.”
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Breiman [286] was the first to analyze the variable importance within Random Forests.”
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015, neutral): “A P] 5 N ov 2 01 5 The Annals of Applied Statistics 2015, Vol. 9, No. 3, 1350–1371 DOI: 10.1214/15-AOAS848 c© Institute of Mathematical Statistics, 2015”
Interpreting tree ensembles with inTrees (2014, methodology): “we compare it to the popularly used decision tree method in the rpart package [6,33] on 20 data sets from UCI [26].”
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (2019, methodology): “Our work most closely relates to the work in (Bertsimas and Dunn 2017) which designs optimal classification trees using MIP, yielding average absolute improvements in out-of-sample accuracy over the state-of-art CART algorithm (Breiman et al. 1984) in the range 1–5%.”
An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, background): “A surrogate model describes the relationship between inputs (i.e., model's adjustable parameters) and outputs (i.e., the performance measure of the simulation model).”
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, methodology): “In particular, the base learners originally used by Breiman [2001a], namely CART trees grown up to a leaf size k [Breiman et al., 1984], are k-PNN predictors.”
Exponentially Increasing the Capacity-to-Computation Ratio for Conditional Computation in Deep Learning (2014, background): “In contrast, there are machine learning models, such as decision trees (Breiman et al., 1984), with a much more favorable ratio: with N computations, a decision tree can actively select O(N) parameters out of a pool of up to O(2 ).”
PREDICTIVE LEARNING VIA RULE ENSEMBLES (2008, background): “Although, in principle, most of these procedures can be used with other base learners, they have almost exclusively been applied with decision trees [ Breiman et al. (1983) , Quinlan (1993)].”
Interpreting Blackbox Models via Model Extraction (2017, methodology): “We compare to CART trees (Breiman et al., 1984) and born-again trees (Breiman & Shang, 1996), rule lists (Letham et al.”
Impact of license choice on Open Source Software development activity (2009, unknown): “IntheGiniindex( Breiman,Friedman,&Olshen,1984 ),if”
Generating Production Rules from Decision Trees (1987, unknown): “Modern systems of this type, such as those described in ( Breiman, Friedman, Olshen and Stone, 1984; Kononenko, Bratko and Roskar, 1984; Quinlan, 1986) can deal effectively with large training sets affected by noise and incom pleteness, and can classify new cases even when the outcome of crucial t”
Varieties of Currency Crises (2003, unknown): “19 See also, Gourinchas, Landerretche, and Valdés (2002) and Schneider and Tornell (2003) for an analysis of the relationship between booms and busts in credit markets and crises.”
ALBATROSS : a learning based transportation oriented simulation system (2000, methodology): “C4.5 (Quinlan 1993), CART ( Breiman et al. 1984 ) and CHAID (Kass 1980) are the most widely used heuristics2.”