Abstract
Forest-based classification and prediction is one of the most commonly used nonparametric statistical methods in many scientific and engineering areas, particularly in machine learning and analysis of high-throughput genomic data. In this chapter, we first introduce the construction of random forests and deterministic forests, and then address a fundamental and practical issue on how large the forests need to be.
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Zhang, H., Singer, B.H. (2010). Random and Deterministic Forests. In: Recursive Partitioning and Applications. Springer Series in Statistics, vol 0. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6824-1_6
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DOI: https://doi.org/10.1007/978-1-4419-6824-1_6
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