Classification and Regression Trees

By Leo Breiman, Jerome H. Friedman, Richard A. Olshen, C. Stone. 2000

10,793 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Algorithms & Complexity

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Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “Random: it selects a subset of features randomly and, by using the selected features, generating as many binary splitting criteria as possible depending on the type of the feature [1], [2], [107].”
On the Art and Science of Machine Learning Explanations (2018, background): “e function дGBM in a validation dataset. Decision trees can be represented as directed graphs where the relative positions of input features can provide insight into their importance and interactions [4]. This makes decision trees useful surrogate models. Input features that appear high and often in”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Random forest regression (Breiman, 2001) is an ensemble method consisting of multiple regression trees (Breiman et al., 1984).”
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning (2014, background): “GP results from [20], Nyström from [16], Laplacian from [17], FITC from [15] and RFR from [21].”
Robust Classification for Imprecise Environments (2000, background): “It has been suggested that, for training, high-cost instances can be compensated for by increasing their prevalence in an instance set ( Breiman, Friedman, Olshen, & Stone, 1984 ).”
CONSISTENCY OF CROSS VALIDATION FOR COMPARING REGRESSION PROCEDURES (2007, neutral): “[3] Breiman, L.”
Patterns of conifer regeneration following high severity wildfire in ponderosa pine - dominated forests of the Colorado Front Range (2016, methodology): “Regression tree analyses were conducted in R 2.14.12 (R Core Team, 2014) using the CTREE function of the PARTY package (Breiman et al., 1984; Hothorn et al., 2006).”
Learning model trees from evolving data streams (2010, methodology): “A very useful analytical tool for evaluating the quality of models is the bias-variance decomposition of the mean squared error (Breiman 1998).”
Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula (2009, background): “The heterogeneity of a node is defined with a deviance notion that can be interpreted as the deviance of a multinomial model (Breiman et al. 1984).”