Abstract
Random Forests were introduced by Leo Breiman [6] who was inspired by earlier work by Amit and Geman [2]. Although not obvious from the description in [6], Random Forests are an extension of Breiman’s bagging idea [5] and were developed as a competitor to boosting. Random Forests can be used for either a categorical response variable, referred to in [6] as “classification,” or a continuous response, referred to as “regression.” Similarly, the predictor variables can be either categorical or continuous.
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Cutler, A., Cutler, D.R., Stevens, J.R. (2012). Random Forests. In: Zhang, C., Ma, Y. (eds) Ensemble Machine Learning. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9326-7_5
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DOI: https://doi.org/10.1007/978-1-4419-9326-7_5
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