Machine Learning, Decision Tree, Information Gain, Entropy.
(probability of a sample in the same training class with class 2) E = -(9/14 * log₂(9/14) + (5/14) * log₂(5/14)) E = 0.94 Information Gain The boundaries of root, nodes and leaves are defined by Information Gain (IG). Gain(S,A) = E(before) - G(after_splitting) Note: You can find ID3 as C4.5 in WEKA