We present a new regression algorithm called Additive Groves and show empirically that it is superior in performance to a number of other established regression methods. A single Grove is an additive model containing a small number of large trees. Trees added to a Grove are trained on the residual error of other trees already in the model. We begin the training process with a single small tree ...

Estimating urban trees growth, especially tree height is very important in urban landscape management. The aim of the study was to predict of tree height base on tree diameter. To achieve this goal, 921 trees from five species were measured in five areas of Mashhad city in 2014. The evaluated trees were ash tree (Fraxinus species), plane tree (Platanus hybrida), white mulberry (Morus alba), ail...

Reliable estimation of discharge in a river is the crucial component of efficient flood management and surface water planning. Hydrologists use historical data to establish a relationship between water level and discharge, which is known as a rating curve. Once a relationship is established it can be used for predicting discharge from future measurements of water level only. Successful applicat...

This paper presents an adaptation of the peepholing method to regression trees. Peepholing was described as a means to overcome the major computational bottleneck of growing classification trees by Catlett [3]. This method involves two major steps: shortlisting and blinkering. The former has the goal of eliminating some continuous variables from consideration when growing the tree, while the se...

Pruning is a method for reducing the error and complexity of induced trees. There are several approaches to pruning decision trees, while regression trees have attracted less attention. We propose a method for pruning regression trees based on the sound foundations of the MDL principle. We develop coding schemes for various constructs and models in the leaves and empirically test the new method...

In this paper we challenge three of the underlying principles of CART, a well know approach to the construction of classification and regression trees. Our primary concern is with the penalization strategy employed to prune back an initial, overgrown tree. We reason, based on both intuitive and theoretical arguments, that the pruning rule for classification should be different from that used fo...

Bayesian Additive Regression Trees (BART) is a Bayesian approach to flexible non-linear regression which has been shown to be competitive with the best modern predictive methods such as those based on bagging and boosting. BART offers some advantages. For example, the stochastic search Markov Chain Monte Carlo (MCMC) algorithm can provide a more complete search of the model space and variation ...

Previous algorithms for constructing regression tree models for longitudinal and multiresponse data have mostly followed the CART approach. Consequently, they inherit the same selection biases and computational difficulties as CART. We propose an alternative, based on the GUIDE approach, that treats each longitudinal data series as a curve and uses chi-squared tests of the residual curve patter...

We develop a Bayesian “sum-of-trees” model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by en...