The Iterative Dichotomiser 3 (ID3) algorithm is one of the most popular designs of the decision induction tree. It is not tolerant of missing values or noisy, and the value of attributes must come from an infinite fixed set.
ID3 uses entropy to calculate the homogeneity of a sample and also for the split. The information gain
G for each attribute
A is computed using the following equation. The root of the final tree is assigned with an attribute with the highest information gain. Then the new subtree is built recursively upon each value of the attribute bound to the root.
With the play ...