Abstract Background and goal The Random Forest (RF) algorithm for regression and
classification has considerably gained popularity since its introduction in 2001. Meanwhile, it …

Abstract Background The Random Forest (RF) algorithm for supervised machine learning is
an ensemble learning method widely used in science and many other fields. Its popularity …

The random forest (RF) algorithm by Leo Breiman has become a standard data analysis tool
in bioinformatics. It has shown excellent performance in settings where the number of …

Background Random Forests is a popular classification and regression method that has
proven powerful for various prediction problems in biological studies. However, its …

Random forests have emerged as a versatile and highly accurate classification and
regression methodology, requiring little tuning and providing interpretable outputs. Here, we …

Modern biology has experienced an increased use of machine learning techniques for large
scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest …

Random Forest (RF) is a powerful supervised learner and has been popularly used in many
applications such as bioinformatics. In this work we propose the guided random forest (GRF) …

Random forests have emerged as one of the most commonly used nonparametric statistical
methods in many scientific areas, particularly in analysis of high throughput genomic data. A …

Random forests (RF) is a popular tree-based ensemble machine learning tool that is highly
data adaptive, applies to “large p, small n” problems, and is able to account for correlation as …

Background Microarray data are often used for patient classification and gene selection. An
appropriate tool for end users and biomedical researchers should combine user friendliness …