Random forest classification is a popular machine learning method for developing prediction
models in many research settings. Often in prediction modeling, a goal is to reduce the …

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 …

Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …

Modern supervised machine learning algorithms involve hyperparameters that have to be
set before running them. Options for setting hyperparameters are default values from the …

The number of trees T in the random forest (RF) algorithm for supervised learning has to be
set by the user. It is unclear whether T should simply be set to the largest computationally …

In recent years, a large amount of model-agnostic methods to improve the transparency,
trustability, and interpretability of machine learning models have been developed. Based on …

The interpretation of feature importance in machine learning models is challenging when
features are dependent. Permutation feature importance (PFI) ignores such dependencies …

The fields of both Natural Language Processing (NLP) and Automated Machine Learning
(AutoML) have achieved remarkable results over the past years. In NLP, especially Large …

Machine learning research depends on objectively interpretable, comparable, and
reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites …

Multi-omics data, that is, datasets containing different types of high-dimensional molecular
variables, are increasingly often generated for the investigation of various diseases …