The random forest (RF) algorithm has several hyperparameters that have to be set by the
user, for example, the number of observations drawn randomly for each tree and whether …

Abstract Cross-Validation (CV), and out-of-sample performance-estimation protocols in
general, are often employed both for (a) selecting the optimal combination of algorithms and …

Clinical prediction models estimate an individual's risk of a particular health outcome. A
developed model is a consequence of the development dataset and model‐building …

Acute respiratory failure (ARF) is a common problem in medicine that utilizes significant
healthcare resources and is associated with high morbidity and mortality. Classification of …

In the past decades, data-driven machine-learning (ML) models have emerged as promising
tools for short-term streamflow forecasting. Among other qualities, the popularity of ML …

Background: Penalization and regularization techniques for statistical modeling have
attracted increasing attention in biomedical research due to their advantages in the …

Plants have evolved various mechanisms to adapt to adverse environmental stresses, such
as the modulation of gene expression. Expression of stress-responsive genes is controlled …

Although interaction effects can be exploited to improve predictions and allow for valuable
insights into covariate interplay, they are given limited attention in analysis. Interaction …

BACKGROUND AND OBJECTIVES: Zeolite has been recognized as a potential adsorbent
for heavy metals in water. The form of zeolite that is generally available in powder has …

Statistical learning methods offer a promising approach for low-flow regionalization. We
examine seven statistical learning models (Lasso, linear, and nonlinear-model-based …