Information Gain In Decision Trees
In information theory and machine learning, information gain is an alternative synonym for 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 information gain should be preferred to other attributes.
Read more about Information Gain In Decision Trees: General Definition, Formal Definition, Drawbacks, Constructing A Decision Tree Using Information Gain
Famous quotes containing the words information, gain, decision and/or trees:
“Rejecting all organs of information ... but my senses, I rid myself of the Pyrrhonisms with which an indulgence in speculations hyperphysical and antiphysical so uselessly occupy and disquiet the mind.”
—Thomas Jefferson (1743–1826)
“Do not gain basely; base gain is equal to ruin.”
—Hesiod (c. 8th century B.C.)
“The women of my mother’s generation had, in the main, only one decision to make about their lives: who they would marry. From that, so much else followed: where they would live, in what sort of conditions, whether they would be happy or sad or, so often, a bit of both. There were roles and there were rules.”
—Anna Quindlen (20th century)
“The trees are coming into leaf
Like something almost being said....”
—Philip Larkin (1922–1986)