Classification and regression trees can identify 6-month mortality risk for patients with advanced hepatocellular carcinoma.

Decision tree regression is a fundamental technique that can be used by itself, and is also the basis for powerful ensemble techniques (a collection of many decision trees), notably, AdaBoost ...

Bayesian Additive Regression Trees (BART) is a flexible machine learning algorithm capable of capturing nonlinearities between an outcome and covariates and interactions among covariates.

Since their inception in the 1980s, regression trees have been one of the more widely used nonparametric prediction methods. Tree-structured methods yield a histogram reconstruction of the regression ...

Random forest regression is an integrated learning method that combines multiple decision tree models into a more powerful model that can effectively avoid overfitting problems and can handle ...

Regression trees are applied to evaluate system performance – using two water quality and two economic performance metrics. Regression trees facilitated insights into the significance of uncertain ...

Decision tree Decision trees (DTs) are a non-parametric supervised learning method used for both classification and regression.