Abstract
Decision trees are models that process data to split it in strategic places to divide the data into groups with high probabilities of one outcome or another. It is especially effective at data with categorical outcomes, but can also be applied to continuous data, such as the time series we have been considering. Decision trees consist of nodes, or splits in the data defined as particular cutoffs for a particular independent variable, and leaves, which are the outcome. For categorical data, the outcome is a class. For continuous data, the outcome is a continuous number, usually some average measure of the dependent variable.
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Reference
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Elsevier, Amsterdam
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Olson, D.L., Wu, D. (2017). Regression Tree Models. In: Predictive Data Mining Models. Computational Risk Management. Springer, Singapore. https://doi.org/10.1007/978-981-10-2543-3_5
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DOI: https://doi.org/10.1007/978-981-10-2543-3_5
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