In this chapter, we looked at how a decision tree ID3 algorithm first constructs a decision tree from the input data and then classifies a new data instance using the constructed tree. The decision tree was constructed by selecting the attribute for branching with the highest information gain. We studied how information gain measures the amount of information that can be learned in terms of the gain in information entropy.
We also learned that the decision tree algorithm can achieve a different result from other algorithms, such as Naive Bayes.
In the next chapter, we will learn how to combine various algorithms or classifiers into a decision forest (called random forest) in order to achieve a more accurate result.