How to calculate information gain in ID3?
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05-11-2019 - |
Pergunta
I am trying to implement a decision tree classifier using ID3 algorithm. According to Aritificial Intelligence - A Modern Approach, information gain of attribute A
is given by:
Gain(A) = B(p/p+n) - Remainder(A)
where B
is the entropy of a Boolean random variable and p
and n
are the number of positive and negative examples in the training set.
My question is:
do p
and n
always refer to examples in the full dataset, or the remaining examples in current partition of the set?
If the former applied, the value of B
would remain fixed throughout the training procedure. Is this correct?
Nenhuma solução correta
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