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Vocabulary of natural language processing

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Concept information

... > NLP methods and tools > artificial intelligence > machine learning > machine learning model > decision tree > random forest
NLP methods and tools > NLP algorithm > classification algorithm > decision tree > random forest

Preferred term

random forest  

Definition

  • An ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. (Wikipedia)

Broader concept

Definitional context(s)

  • Random forest is a supervised learning technique that ensembles independent decision trees to yield a result. (Yim, Lee, Verma, Hickmann, Zhu, Sallade, Ng, Chi & Liu, 2022)
  • The random forest is an ensemble classifier that returns the mode of the class predictions of several decision trees. (Yancheva & Rudzicz, 2013)

Example

  • Afterwards in the test phase the random forest makes predictions using the outputs of the same individual classifiers. (Uzdilli, Jaggi, Egger, Julmy, Derczynski & Cieliebak, 2015)
  • The random forest uses the outputs of individual classifiers as features and the labels on the training data as input for training. (Uzdilli, Jaggi, Egger, Julmy, Derczynski & Cieliebak, 2015)

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URI

http://data.loterre.fr/ark:/67375/8LP-L33JN8VT-W

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