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Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior…

Standard supervised learning procedures are validated against a test set that is assumed to have come from the same distribution as the training data. However, in many problems, the test data may have come from a different distribution. We…

Machine Learning · Statistics 2019-08-28 Tim Coleman , Kimberly Kaufeld , Mary Frances Dorn , Lucas Mentch

We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component within arbitrary computation graphs that are optimized…

Machine Learning · Statistics 2018-03-02 Nathan Lay , Adam P. Harrison , Sharon Schreiber , Gitesh Dawer , Adrian Barbu

Random Forests (RF) is a popular machine learning method for classification and regression problems. It involves a bagging application to decision tree models. One of the primary advantages of the Random Forests model is the reduction in…

Machine Learning · Statistics 2022-07-06 Sai K Popuri

Regression trees and random forests are popular and effective non-parametric estimators in practical applications. A recent paper by Athey and Wager shows that the random forest estimate at any point is asymptotically Gaussian; in this…

Econometrics · Economics 2021-02-02 Kevin Li

Random forests are among the most popular classification and regression methods used in industrial applications. To be effective, the parameters of random forests must be carefully tuned. This is usually done by choosing values that…

Machine Learning · Statistics 2018-07-03 C. H. Bryan Liu , Benjamin Paul Chamberlain , Duncan A. Little , Angelo Cardoso

Survival random forest is a popular machine learning tool for modeling censored survival data. However, there is currently no statistically valid and computationally feasible approach for estimating its confidence band. This paper proposes…

Methodology · Statistics 2022-04-27 Sarah Elizabeth Formentini , Wei Liang , Ruoqing Zhu

Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursively cutting a domain. The flexibility of these methods is…

Machine Learning · Statistics 2019-12-03 Shufei Ge , Shijia Wang , Yee Whye Teh , Liangliang Wang , Lloyd T. Elliott

Random forest (RF) methodology is one of the most popular machine learning techniques for prediction problems. In this article, we discuss some cases where random forests may suffer and propose a novel generalized RF method, namely…

Machine Learning · Statistics 2019-04-24 Haozhe Zhang , Dan Nettleton , Zhengyuan Zhu

We study various types of consistency of honest decision trees and random forests in the regression setting. In contrast to related literature, our proofs are elementary and follow the classical arguments used for smoothing methods. Under…

Methodology · Statistics 2026-05-21 Martin Bladt , Rasmus Frigaard Lemvig

We develop a finite-sample, design-based theory for random forests in which each tree is a randomized conditional predictor acting on fixed covariates and the forest is their Monte Carlo average. An exact variance identity separates Monte…

Machine Learning · Statistics 2026-03-03 Nathaniel S. O'Connell

Random forests, introduced by Leo Breiman in 2001, are a very effective statistical method. The complex mechanism of the method makes theoretical analysis difficult. Therefore, a simplified version of random forests, called purely random…

Statistics Theory · Mathematics 2010-07-28 Robin Genuer

We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to…

Machine Learning · Statistics 2015-02-23 Feng Nan , Joseph Wang , Venkatesh Saligrama

We propose a computationally efficient algorithm for gradient-based linear dimension reduction and high-dimensional regression. The algorithm initially computes a Mondrian forest and uses this estimator to identify a relevant feature…

Statistics Theory · Mathematics 2024-07-16 Ricardo Baptista , Eliza O'Reilly , Yangxinyu Xie

Random forest regression is a powerful non-parametric method that adapts to local data characteristics through data-driven partitioning, making it effective across diverse application domains. However, the piecewise constant nature of…

Machine Learning · Computer Science 2026-05-19 Ziyi Liu , Phuc Luong , Mario Boley , Daniel F. Schmidt

Decision tree learning is increasingly being used for pointwise inference. Important applications include causal heterogenous treatment effects and dynamic policy decisions, as well as conditional quantile regression and design of…

Machine Learning · Statistics 2024-02-08 Matias D. Cattaneo , Jason M. Klusowski , Peter M. Tian

Random forests are ensemble learning methods introduced by Breiman (2001) that operate by averaging several decision trees built on a randomly selected subspace of the data set. Despite their widespread use in practice, the respective roles…

Statistics Theory · Mathematics 2016-03-15 Roxane Duroux , Erwan Scornet

Random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of…

Quantitative Methods · Quantitative Biology 2007-05-23 Ramon Diaz-Uriarte , Sara Alvarez de Andres

Random forests are a statistical learning technique that use bootstrap aggregation to average high-variance and low-bias trees. Improvements to random forests, such as applying Lasso regression to the tree predictions, have been proposed in…

Machine Learning · Statistics 2025-11-13 Jing Shang , James Bannon , Benjamin Haibe-Kains , Robert Tibshirani

This paper examines from an experimental perspective random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001. It first aims at confirming, known but sparse,…

Machine Learning · Statistics 2008-11-24 Robin Genuer , Jean-Michel Poggi , Christine Tuleau