Machine learning has shown utility in detecting patterns within large, unstructured, and
complex datasets. One of the promising applications of machine learning is in precision …

Measuring variable importance for computational models or measured data is an important
task in many applications. It has drawn our attention that the variable importance analysis …

Background Random forest (RF) is a machine-learning method that generally works well
with high-dimensional problems and allows for nonlinear relationships between predictors; …

Identifying adaptive loci can provide insight into the mechanisms underlying local
adaptation. Genotype–environment association (GEA) methods, which identify these loci …

Genome-wide association studies (GWASs) have become the focus of the statistical analysis
of complex traits in humans, successfully shedding light on several aspects of genetic …

While shallow decision trees may be interpretable, larger ensemble models like gradient-
boosted trees, which often set the state of the art in machine learning problems involving …

Random Forests are frequently applied as they achieve a high prediction accuracy and have
the ability to identify informative variables. Several approaches for variable selection have …

Background Random forests have often been claimed to uncover interaction effects.
However, if and how interaction effects can be differentiated from marginal effects remains …

Most statistical models assume linearity and few variable interactions, even though real‐
world ecological patterns often result from nonlinear and highly interactive processes. We …

Increasing accumulation of nanoplastics across ecosystems poses a significant threat to
both terrestrial and aquatic life. Surface-enhanced Raman scattering (SERS) is an emerging …