Decision Tree

It is a kind of supervised machine learning algorithm. It has a tree structure with a root node and many leaf nodes. Every node represents a feature and the links between the nodes represent the decision. Every leaf node represents a result or classification or solution.

A decision tree produces a sequence of rules that can be used to classify the data based on the attributes of classes.

Main advantages of decision tree are that these are simple to understand and visualize. It can be used with numerical or categorical data. It can be used successfully even with small data.

The main disadvantage is that sometimes based on different variations in the data, complex tree may be generated.

There are many applications of decision trees:

1. Detection of frauds

2. Diagnosis of diseases

3. Diagnosis of any faults in any machinery

4. Identification of emotions

5. Selecting a flight for traveling