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 …
S Pölsterl - Journal of Machine Learning Research, 2020 - jmlr.org
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 …
Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation …
AL Boulesteix, S Janitza, J Kruppa… - … Reviews: Data Mining …, 2012 - Wiley Online Library
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 …
MW Conklin, JC Eickhoff, KM Riching… - The American journal of …, 2011 - Elsevier
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 …
There is an urgent need to develop effective vulnerability assessments for evaluating the conservation status of species in a changing climate. Several new assessment approaches …