Additive tree methods are widely used in ecology. To date most ecologists have boosted regression (BRT) methods. However, Bayesian additive (BART) models may offer advantages to previously unexamined. Here we test whether BART has some benefits over the BRT method. do this use two grassland data and 13 hydroclimatic land predictor variables. The dataset contained from a period of drought as wel...

Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, it is unclear whether such an ecologically unrealistic model can provide accurate insights on growt...

Boosting is a means of constructing a strong classi er by aggregating a sequence of weaker classi ers. The sequence evolves by focusing the attention of a base learning algorithm on remaining examples that are hardest to classify. Boosting algorithms can thus be used to enhance the performance of a simple classi er (typically a regression tree or tree stump) in an automatic way. This makes boos...

With this research, we sought to examine the performance of six different regression tree data mining methods to predict mortality in head injury. Using a data set consisting of 1603 head injury cases, we assessed the performance of: the Classification and Regression Trees (CART) method; the Chi-squared Automatic Interaction Detector (CHAID) method; the Exhaustive CHAID (E-CHAID) method; the Qu...

--------------------------------------------------------***--------------------------------------------------------Abstract Physicians classify patients into those with or without a specific disease. Classification trees are frequently used to classify patients according to the presence or absence of a disease. In the data-mining and machine learning, alternate classification schemes have been ...