Decision Tree
What did you do?
To get a deeper insight into creating decision trees on the laptop, I started to inform myself about possible supporting tools that can be used. As I am using an Apple MacBook, I found out that the software “XMind” cannot just help for drawing decision trees, but also for developing flowing charts, mind maps or to-do-lists. I thought about using Microsoft Excel as a tool for sorting the data. However, I finally looked up the necessary information in the given table without using any automatic sorting function, as for me, it was easier to manually type the data into MS Excel.
After installing the software and reading the task description, I realized that the tool is pretty easy to use and that it is very helpful in structuring information, as I will explain later on in this write-up. When creating the decision tree I started with entering the existing data. By analyzing the data in a first view you can directly see that the first and last name does not have any influence on the loan grant respectively the loan amount, which seems to be self-explaining. It makes sense to start with the node with the highest number of different characteristics. This way the tree will become clearer. That’s why I started with the distinction of the age and afterwards chronologically with the loan type, the ability to pay and finally the past payment record. The loan amount that already includes the information whether a loan was granted (loan amount > 0 $) or not (loan amount = 0 $), was placed under each line of the tree. This results in a total of 72 paths to get to a loan amount as a consequence of the characteristics of the 4 named criteria.
However I quickly found out that the data set does not describe all of the 72 possible combinations of the criteria. Therefore, I used rational arguments to figure out a possible arguable solution that will be described in the next section of this write-up. This supplemented information can be recognized by the red
To get a deeper insight into creating decision trees on the laptop, I started to inform myself about possible supporting tools that can be used. As I am using an Apple MacBook, I found out that the software “XMind” cannot just help for drawing decision trees, but also for developing flowing charts, mind maps or to-do-lists. I thought about using Microsoft Excel as a tool for sorting the data. However, I finally looked up the necessary information in the given table without using any automatic sorting function, as for me, it was easier to manually type the data into MS Excel.
After installing the software and reading the task description, I realized that the tool is pretty easy to use and that it is very helpful in structuring information, as I will explain later on in this write-up. When creating the decision tree I started with entering the existing data. By analyzing the data in a first view you can directly see that the first and last name does not have any influence on the loan grant respectively the loan amount, which seems to be self-explaining. It makes sense to start with the node with the highest number of different characteristics. This way the tree will become clearer. That’s why I started with the distinction of the age and afterwards chronologically with the loan type, the ability to pay and finally the past payment record. The loan amount that already includes the information whether a loan was granted (loan amount > 0 $) or not (loan amount = 0 $), was placed under each line of the tree. This results in a total of 72 paths to get to a loan amount as a consequence of the characteristics of the 4 named criteria.
However I quickly found out that the data set does not describe all of the 72 possible combinations of the criteria. Therefore, I used rational arguments to figure out a possible arguable solution that will be described in the next section of this write-up. This supplemented information can be recognized by the red