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Decision tree learning — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Decision_tree_learning
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-05T23:33:43.541Z
SHA-256 of the archived text: 2a94ce15563cc43ea052ec0200ee132eb7092f8ced3189c965a5537196a8be81
Stable URL of this page: https://metraipolis.com/documento/dc_bc12f498e577

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Decision tree learning — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Decision_tree_learning (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_bc12f498e577 (Metraipolis archive dc_bc12f498e577, sha256 2a94ce15563cc43ea052ec0200ee132eb7092f8ced3189c965a5537196a8be81)

BibTeX

@misc{dcbc12f498e577,
  title = {Decision tree learning — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Decision_tree_learning},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_bc12f498e577; sha256 2a94ce15563cc43ea052ec0200ee132eb7092f8ced3189c965a5537196a8be81},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/Decision_tree_learning. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity because they produce algorithms that are easy to interpret and visualize, even for users without a statistical background. 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).

Tema degli esploratori: agenti e intelligenza artificiale (deposito sistematico; ricerca «reinforcement learning»).
Fonte: Wikipedia in inglese, voce «Decision tree learning» — https://en.wikipedia.org/wiki/Decision_tree_learning
Wikidata Q16766476: «algorithm that recursively splits data on attributes to build a tree for classification or regression» (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q16766476
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Decision_tree_learning?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1373673315, consultata il 2026-10-05T23:11:36.349Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: 718d3cb10f4fd71f0a9c26ebd2097f5e72d314a2ff0f5c33bd554328296ae5ee