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Tree-based models such as decision trees and random forests (RF) are a cornerstone of modern machine-learning practice.
Problems in the analysis of survey data, and a proposal
Morgan, J. N. and Sonquist, J. A · 1963
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A modal search technique for predictive nominal scale multivariate analysis
Messenger, R. and Mandell, L · 1972
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Generalized cross-validation as a method for choosing a good ridge parameter
Golub, G. H., Heath, M., and Wahba, G · 1979
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Classification and regression trees
Breiman, L., Friedman, J., Olshen, R., and Stone, C. J · 1984
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Induction of decision trees
Quinlan, J. R · 1986
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Using the adap learning algorithm to forecast the onset of diabetes mellitus
Smith, J. W., Everhart, J. E., Dickson, W., Knowler, W. C., and Johannes, R. S · 1988
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Classification of radar returns from the ionosphere using neural networks
Sigillito, V. G., Wing, S. P., Hutton, L. V., and Baker, K. B · 1989
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Multivariate adaptive regression splines
Friedman, J. H · 1991
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The population biology of abalone (haliotis species) in tasmania. i. blacklip abalone (h. rubra) from the north coast and islands of bass strait
Nash, W. J., Sellers, T. L., Talbot, S. R., Cawthorn, A. J., and Ford, W. B · 1994
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The effects of exposure to violence on young children (1995)
Osofsky, J. D · 1997
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Sparse spatial autoregressions
Pace, R. K. and Barry, R · 1997
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A classification tree approach to the development of actuarial violence risk assessment tools
Steadman, H. J., Silver, E., Monahan, J., Appelbaum, P., Robbins, P. C., Mulvey, E. P., Grisso, T., Roth, L. H., and Banks, S · 2000
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Random forests
Breiman, L · 2001
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The Elements of Statistical Learning , volume 1
Friedman, J., Hastie, T., Tibshirani, R., et al · 2001
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Greedy function approximation: a gradient boosting machine
Friedman, J. H · 2001
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Random forest: a classification and regression tool for compound classification and qsar modeling
Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., and Feuston, B. P · 2003
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Least angle regression
Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R · 2004
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An empirical comparison of supervised learning algorithms
Caruana, R. and Niculescu-Mizil, A · 2006
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Gene selection and classification of microarray data using random forest
Díaz-Uriarte, R. and De Andres, S. A · 2006
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Uci machine learning repository, 2007
Asuncion, A. and Newman, D · 2007
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An empirical evaluation of supervised learning in high dimensions
Caruana, R., Karampatziakis, N., and Yessenalina, A · 2008
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Identification of children at very low risk of clinically-important brain injuries after head trauma: a prospective cohort study
Kuppermann, N., Holmes, J. F., Dayan, P. S., Hoyle, J. D., Atabaki, S. M., Holubkov, R., Nadel, F. M., Monroe, D., Stanley, R. M., Borgialli, D. A., et al · 2009
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Bart: Bayesian additive regression trees
Chipman, H. A., George, E. I., and McCulloch, R. E · 2010
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Modeling species distribution and change using random forest
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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ranger: A fast implementation of random forests for high dimensional data in C++ and R
Wright, M. N., Ziegler, A., et al · 2017
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iterative random forests to discover predictive and stable high-order interactions
Basu, S., Kumbier, K., Brown, J. B., and Yu, B · 2018
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Data-driven advice for applying machine learning to bioinformatics problems
Olson, R. S., Cava, W. L., Mustahsan, Z., Varik, A., and Moore, J. H · 2018
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Randomization as regularization: a degrees of freedom explanation for random forest success
Mentch, L. and Zhou, S · 2019
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Evans, J. S., Murphy, M. A., Holden, Z. A., and Cushman, S. A · 2011
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Scikit-learn: Machine learning in python
Pedregosa, F., Varoquaux, G. ë. l., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al · 2011
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Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
Boulesteix, A.-L., Janitza, S., Kruppa, J., and König, I. R · 2012
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Random forests for genomic data analysis
Chen, X. and Ishwaran, H · 2012
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Stability
Yu, B · 2013
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Do we need hundreds of classifiers to solve real world classification problems?
Fernández-Delgado, M., Cernadas, E., Barro, S., and Amorim, D · 2014
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C4. 5: programs for machine learning
Quinlan, J. R · 2014
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Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., and Yu, B · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
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Gaining free or low-cost interpretability with interpretable partial substitute
Wang, T · 2019
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Learning epistatic polygenic phenotypes with boolean interactions
Behr, M., Kumbier, K., Cordova-Palomera, A., Aguirre, M., Ashley, E., Butte, A., Arnaout, R., Brown, J. B., Preist, J., and Yu, B · 2020
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Generalized and scalable optimal sparse decision trees
Lin, J., Zhong, C., Hu, D., Rudin, C., and Seltzer, M · 2020
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Pmlb v1. 0: an open source dataset collection for benchmarking machine learning methods
Romano, J. D., Le, T. T., La Cava, W., Gregg, J. T., Goldberg, D. J., Ray, N. L., Chakraborty, P., Himmelstein, D., Fu, W., and Moore, J. H · 2020
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Interpolation can hurt robust generalization even when there is no noise
Donhauser, K., Tifrea, A., Aerni, M., Heckel, R., and Yang, F · 2021
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Bridging Breiman’s brook: From algorithmic modeling to statistical learning
Hooker, G. and Mentch, L · 2021
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Universal consistency of decision trees in high dimensions
Klusowski, J. M · 2021
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Interpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., and Zhong, C · 2021
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imodels: a python package for fitting interpretable models
Singh, C., Nasseri, K., Tan, Y. S., Tang, T., and Yu, B · 2021
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Tan, Y. S., Agarwal, A., and Yu, B · 2021
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Fast interpretable greedy-tree sums (figs)
Tan, Y. S., Singh, C., Nasseri, K., Agarwal, A., and Yu, B · 2022
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