Breiman L, Friedmanm J, Olshenm R, Stone CJ (1984) 'Classification and regression trees
The regression tree
is composed of 11 nodes, six of which are terminal nodes (Table).
Approaches that are applicable to cost estimation range from statistics based multivariable regression analysis to machine-learning techniques such as Classification and Regression Trees
(CART), M5 model tree (M5-MT), Artificial Neu ral Network (ANN), Support Vector Machines (SVM), and Least Squares Support Vector Machine (LS-SVM).
A regression tree
using distance from the Rio Grande and maximum depth explained 34% of the variation in abundance of N.
We explored 2 different predictive modeling approaches using past survey counts as the response data: 1) we fit a Poisson regression model with linear and additive effects of land-cover variables (on the log scale); 2) we formed predictions using boosted regression trees
(hereafter 'boosted trees'), a non-parametric method that tends to perform well in settings where interactions are prevalent or the relationship between response and predictor variables is highly non-linear (De'ath 2007, Elith et al.
Nonlinear regression tree
analysis was used to investigate nonlinear effects of site, strain, and environmental conditions (mean seasonal temperature and salinity).
The purpose of this study is to explore the performance of credit scoring using discriminant analysis, logistic regression, neural networks and classification and regression tree
We first performed a classification and regression tree
(CART) analysis using AnswerTree SPSS[R] version 3.
A RandomForest regression tree
analysis was performed (using the RandomForest algorithm in R (Prasad and others 2006)) on the total number of exit holes, epicormic shoots, and canopy transmission against the ATHI value.
The purpose of this study was to analyze characteristics of prisoners who engaged in high-risk behaviors prior to incarceration using classification and regression tree
analysis in order to identify groups at varying levels of HIV risk behaviors.
Hence, the data used to build the classification and regression tree
is either in the form of binned data or nominal data.
The expanded MDR method involves classification into two groups using a regression tree