Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …

Objective: Machine learning classification has been the most important computational
development in the last years to satisfy the primary need of clinicians for automatic early …

This is a book on data analysis with a specific focus on the practice of predictive modeling.
The term predictive modeling may stir associations such as machine learning, pattern …

Background Variable importance measures for random forests have been receiving
increased attention as a means of variable selection in many classification tasks in …

Background Random forests are becoming increasingly popular in many scientific fields
because they can cope with" small n large p" problems, complex interactions and even …

Recursive partitioning methods have become popular and widely used tools for
nonparametric regression and classification in many scientific fields. Especially random …

Fifty years have passed since the publication of the first regression tree algorithm. New
techniques have added capabilities that far surpass those of the early methods. Modern …

Marine protected areas (MPAs) are increasingly being used globally to conserve marine
resources. However, whether many MPAs are being effectively and equitably managed, and …

Microarray studies yield data sets consisting of a large number of candidate predictors
(genes) on a small number of observations (samples). When interest lies in predicting …

Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that
66% of approved human drugs listed in the DrugBank database target a cell-surface protein …