Decision tree Basics

1. Advantages: low computing complexity, easy to understand output results, insensitive to missing median values, and the ability to process irrelevant feature data.

Disadvantage: excessive matching may occur. Applicable data types: numeric and nominal

2. The K-Nearest Neighbor Algorithm can complete many classification tasks, but its biggest drawback is that it cannot give the internal meaning of data. The main advantage of decision tree is that the data form is very easy to understand.

Decision tree Basics