Alternating Decision Tree Algorithm: Difference between revisions
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* (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/alternating_decision_tree Retrieved:2017-11-7. | * (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/alternating_decision_tree Retrieved:2017-11-7. | ||
** An '''alternating decision tree''' (ADTree) is a [[machine learning method]] for classification. It generalizes [[Decision tree learning|decision trees]] and has connections to [[boosting (machine learning)|boosting]]. <P> | ** An '''alternating decision tree''' (ADTree) is a [[machine learning method]] for classification. It generalizes [[Decision tree learning|decision trees]] and has connections to [[boosting (machine learning)|boosting]]. <P> An ADTree consists of an alternation of decision nodes, which specify a predicate condition, and prediction nodes, which contain a single number. An instance is classified by an ADTree by following all paths for which all decision nodes are true, and summing any prediction nodes that are traversed. | ||
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Latest revision as of 23:01, 17 August 2021
An Alternating Decision Tree Algorithm is a decision tree algorithm that ...
References
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/alternating_decision_tree Retrieved:2017-11-7.
- An alternating decision tree (ADTree) is a machine learning method for classification. It generalizes decision trees and has connections to boosting.
An ADTree consists of an alternation of decision nodes, which specify a predicate condition, and prediction nodes, which contain a single number. An instance is classified by an ADTree by following all paths for which all decision nodes are true, and summing any prediction nodes that are traversed.
- An alternating decision tree (ADTree) is a machine learning method for classification. It generalizes decision trees and has connections to boosting.