This article is about decision trees in machine learning. For the use of the term in decision analysis, see Decision tree.
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Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.
Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. More generally, the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities such as categorical sequences.[1]
Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity.[2]
In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data (but the resulting classification tree can be an input for decision making).
^Studer, Matthias; Ritschard, Gilbert; Gabadinho, Alexis; Müller, Nicolas S. (2011). "Discrepancy Analysis of State Sequences". Sociological Methods & Research. 40 (3): 471–510. doi:10.1177/0049124111415372. ISSN 0049-1241. S2CID 13307797.
^Wu, Xindong; Kumar, Vipin; Ross Quinlan, J.; Ghosh, Joydeep; Yang, Qiang; Motoda, Hiroshi; McLachlan, Geoffrey J.; Ng, Angus; Liu, Bing; Yu, Philip S.; Zhou, Zhi-Hua (2008-01-01). "Top 10 algorithms in data mining". Knowledge and Information Systems. 14 (1): 1–37. doi:10.1007/s10115-007-0114-2. hdl:10983/15329. ISSN 0219-3116. S2CID 2367747.
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