Decision trees are an example of classification technique. Here, we classify data in a tree format using data features or attributes. Since decision trees depict the flows and possible outcome for each flow, they are used in identifying the best strategy to reach the goal.
In decision trees, we start with testing an attribute and split the data based on that attribute:
We continue with the process.
We can build multiple decision trees for the same problem.
The efficiency and size of the tree is directly proportional to the attributes chosen by us.
We also need to have termination criteria:
One obvious criterion is that all the records at the node belong to one class and hence...