There are a number of learning methods that provide solutions to classification and regression problems, including Linear Regression, Decision Trees, KNN, and SVMs. These methods work well in many applications, but they are challenged for real world problems that are noisy, nonlinear or high dimensional. Furthermore, missing data (e.g., missing historical features of companies in stock data), i...

BACKGROUND Characterizing multipollutant health effects is challenging. We use classification and regression trees to identify multipollutant joint effects associated with pediatric asthma exacerbations and compare these results with those from a multipollutant regression model with continuous joint effects. METHODS We investigate the joint effects of ozone, NO2 and PM2.5 on emergency departm...

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...

We put forward a new method of growing regression trees via maximum likelihood. It inherits the CART (Brieman et al., 1984) backward fitting idea. However, standard likelihood based methods such as model selection criteria and likelihood ratio tests are naturally incorporated into each stage of the tree procedure. Compared with other least squared tree methods, maximum likelihood regression tre...

The regression tree [1] has been used as a tool for exploring multivariate data sets for some time. As in multiple linear regression, the technique is applied to a data set consisting of a continuous response variable y and a set of predictor variables {x1, x2, ..., xk} which may be continuous or categorical. However, instead of modelling y as a linear function of the predictors, regression tre...

In many real-world domains the task of machine learning algorithms is to learn a theory predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and c...