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- Information_gain_in_decision_trees abstract "In information theory and machine learning, information gain is a synonym for Kullback–Leibler divergence. However, in the context of decision trees, the term is sometimes used synonymously with mutual information, which is the expectation value of the Kullback–Leibler divergence.In particular, the information gain about a random variable X obtained from an observation that a random variable A takes the value A=a is the Kullback-Leibler divergence DKL(p(x | a) || p(x | I)) of the prior distribution p(x | I) for x from the posterior distribution p(x | a) for x given a. The expected value of the information gain is the mutual information I(X; A) of X and A – i.e. the reduction in the entropy of X achieved by learning the state of the random variable A. In machine learning, this concept can be used to define a preferred sequence of attributes to investigate to most rapidly narrow down the state of X. Such a sequence (which depends on the outcome of the investigation of previous attributes at each stage) is called a decision tree. Usually an attribute with high mutual information should be preferred to other attributes.".
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- Information_gain_in_decision_trees subject Category:Classification_algorithms.
- Information_gain_in_decision_trees subject Category:Decision_trees.
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- Information_gain_in_decision_trees type Algorithm105847438.
- Information_gain_in_decision_trees type ClassificationAlgorithms.
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- Information_gain_in_decision_trees type Procedure101023820.
- Information_gain_in_decision_trees type PsychologicalFeature100023100.
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- Information_gain_in_decision_trees comment "In information theory and machine learning, information gain is a synonym for Kullback–Leibler divergence.".
- Information_gain_in_decision_trees label "Information gain in decision trees".
- Information_gain_in_decision_trees sameAs Q17083041.
- Information_gain_in_decision_trees sameAs Q17083041.
- Information_gain_in_decision_trees sameAs Information_gain_in_decision_trees.
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