Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics by Anne-Laure Boulesteix, Silke Janitza, Jochen Kruppa, Inke R. König
(Boulesteix, A.-L., Janitza, S., Kruppa, J. and König, I. R. (2012), Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. WIREs Data Mining Knowl Discov, 2: 493–507. doi: 10.1002/widm.1072)
Abstract:
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 variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and return measures of variable importance. This paper synthesizes 10 years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is paid to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research.
Something to expand your horizons a bit.
And a new way to say “curse of dimensionality,” to-wit,
‘n ≪ p curse’
New to me anyway.
I was amused to read at the Wikipedia article on random forests that its disadvantages include:
Unlike decision trees, the classifications made by Random Forests are difficult for humans to interpret.
Turn about is fair play since many classifications made by humans are difficult for computers to interpret. 😉