Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …

Fifty years have passed since the publication of the first regression tree algorithm. New
techniques have added capabilities that far surpass those of the early methods. Modern …

scikit-survival is an open-source Python package for time-to-event analysis fully compatible
with scikit-learn. It provides implementations of many popular machine learning techniques …

The general principle of random forests is to aggregate a collection of random decision
trees. The goal is, instead of seeking to optimize a predictor “at once” as for a CART tree, to …

Detecting frauds in credit card transactions is perhaps one of the best testbeds for
computational intelligence algorithms. In fact, this problem involves a number of relevant …

Background Prediction models for time-to-event outcomes are commonly used in biomedical
research to obtain subject-specific probabilities that aid in making important clinical care …

Artificial neural networks have been successfully applied to a variety of machine learning
tasks, including image recognition, semantic segmentation, and machine translation …

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 …

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 …

Evidence for the potent influence of stromal organization and function on invasion and
metastasis of breast tumors is ever growing. We have performed a rigorous examination of …