In longitudinal studies variables are measured repeatedly over time, leading to clustered
and correlated observations. If the goal of the study is to develop prediction models …

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 …

Networks of citizen scientists (CS) have the potential to observe biodiversity and species
distributions at global scales. Yet the adoption of such datasets in conservation science may …

The most commonly used statistical models of civil war onset fail to correctly predict most
occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of …

Identification of subgroups of patients for whom treatment A is more effective than treatment
B, and vice versa, is of key importance to the development of personalized medicine. Tree …

Multiple stressors threaten biodiversity and ecosystem integrity, imposing new challenges to
ecosystem management and restoration. Ecosystem managers are required to address and …

This paper presents an extension of the random forest (RF) method to the case of clustered
data. The proposed 'mixed-effects random forest'(MERF) is implemented using a standard …

Objective We aimed to identify machine learning (ML) models for type 2 diabetes (T2DM)
prediction in community settings and determine their predictive performance. Method …

Abstract Background Diabetes Mellitus (DM) has become the third chronic non-
communicable disease that hits patients after tumors, cardiovascular and cerebrovascular …

Background Machine learning methodologies are gaining popularity for develo** medical
prediction models for datasets with a large number of predictors, particularly in the setting of …