Summary: Background: The concept of boosting emerged from the field of machine learning. The basic idea is to boost the accuracy of a weak classifying tool by combining various instances into a more accurate prediction. This general concept was later adapted to the field of statistical modelling. Nowadays, boosting algorithms are often applied to estimate and select predictor effects in statistical regression models.Objectives: This review article attempts to highlight the evolution of boosting algorithms from machine learning to statistical modelling.Methods: We describe the AdaBoost algorithm for classification as well as the two most prominent statistical boosting approaches, gradient boosting and likelihood-based boosting for statistical modelling. We highlight the methodological background and present the most common software implementations.Results: Although gradient boosting and likelihood-based boosting are typically treated separately in the literature, they share the same methodological roots and follow the same fundamental concepts. Compared to the initial machine learning algorithms, which must be seen as black-box prediction schemes, they result in statistical models with a straight-forward interpretation.Conclusions: Statistical boosting algorithms have gained substantial interest during the last decade and offer a variety of options to address important research questions in modern biomedicine.

Summary: Background: Penalization and regularization techniques for statistical modeling have attracted increasing attention in biomedical research due to their advantages in the presence of high-dimensional data. A special focus lies on algorithms that incorporate automatic variable selection like the least absolute shrinkage operator (lasso) or statistical boosting techniques. Objectives: Focusing on the linear regression framework, this article compares the two most-common techniques for this task, the lasso and gradient boosting, both from a methodological and a practical perspective. Methods: We describe these methods highlighting under which circumstances their results will coincide in low-dimensional settings. In addition, we carry out extensive simulation studies comparing the performance in settings with more predictors than observations and investigate multiple combinations of noise-to-signal ratio and number of true non-zero coeffcients. Finally, we examine the impact of different tuning methods on the results. Results: Both methods carry out penalization and variable selection for possibly highdimensional data, often resulting in very similar models. An advantage of the lasso is its faster run-time, a strength of the boosting concept is its modular nature, making it easy to extend to other regression settings. Conclusions: Although following different strategies with respect to optimization and regularization, both methods imply similar constraints to the estimation problem leading to a comparable performance regarding prediction accuracy and variable selection in practice.

On the basis of extensive research supporting the clinical validity of binge eating disorder (BED), BED has been included in DSM-5 as a distinct eating disorder diagnosis.¹ Binge eating disorder is defined by recurrent objective binge eating (OBE) episodes that occur in the absence of inappropriate weight control behaviors. Binge eating disorder is the most common eating disorder, with a worldwide mean lifetime prevalence rate of 1.9%.² The prevalence estimates substantially increase among individuals with obesity. In contrast to other eating disorders, the sex ratio for BED is less skewed. However, women are more often affected, with an odds ratio of 2.4.² No ethnic or racial group is overrepresented.³ Binge eating disorder is associated with a significantly elevated risk of psychiatric and medical comorbidity and psychosocial impairment.^2,4 Of importance, patients with subthreshold DSM-IV BED do not differ with regard to demographic characteristics, body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared), and a wide range of eating disorder and general psychopathologic findings from individuals with full-syndrome BED.⁵ Binge eating disorder is associated with increases in health care use and costs.^3,6