What is a Decision Tree?
A Decision tree is a type of supervised learning algorithm that can be used in classification and regressor problems.
The input to a decision tree can be both continuous and categorical.
It works on an if-then statement.
A decision tree tries solving a problem using a tree representation (like Node and Leaf).
Assumption while creating a Decision Tree:
Initially, all the training set is considered as a root.
Feature values are preferred to be categorical, if continuous then they are discretized.
Records are distributed recursively on the basis of attribute values.
Which attributes are considered to be in the root node or internal node is done by using a statistical approach.
