A decision tree is a supervised learning technique that works on the divide-and-conquer approach. It can be used to address both classification and regression. The population undergoes a split into two or more homogeneous samples based on the most significant feature.
For example, let's say we have got a sample of people who applied for a loan from the bank. For this example, we will take the count as 50. Here, we have got three attributes, that is, gender, income, and the number of other loans held by the person, to predict whether to give them a loan or not.
We need to segment the people based on gender, income, and the number of other loans they hold and find out the most significant factor. This tends to create the most ...
