Decision Trees
Learn how to use the decision tree as a classification algorithm.
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Decision trees are commonly used in financial domains due to their ability to solve and explain prediction problems. They are also used as a module in other algorithms to solve more complex algorithms like bagging and boosting.
To understand the decision trees, let’s look at the problem of a car loan defaulter prediction. When we want to get a loan from the bank, they ask some questions and derive answers from our past credit history. Questions may relate to:
- Monthly income
- Personal information
- Previous loans
- Current properties
Let’s say a person has a monthly income of $10K. Their age is 37. They have not taken any previous loans and do not own any property. A decision tree with the past data (from other customers) can be created like this:
This is a very basic example. In the real world, a lot of other and complex data points are considered.
So, according to the data, this person $(Income< ...