Data Science Algorithms in a Week - Second Edition

Swim preference – decision tree construction by the ID3 algorithm

Here, we describe, step by step, how an ID3 algorithm would construct a decision tree from the given data samples in the swim preference example. The initial set consists of six data samples:

S={(none,cold,no),(small,cold,no),(good,cold,no),(none,warm,no),(small,warm,no),(good,warm,yes)}

In the previous sections, we calculated the information gains for both, and the only non- classifying attributes, swimming suit, and water temperature, as follows:

IG(S,swimming suit)=0.3166890883IG(S,water temperature)=0.19087450461

Hence, we would choose the swimming suit attribute as it has a higher information gain. There is no tree drawn yet, so we start from the root node. As the swimming ...