Decision trees graphically show the decisions to be made, the observed events that may occur, and the probabilities of the outcomes given a specific set of observable events occurring together. Decision trees are used as a popular machine learning algorithm. Based on a dataset of observable events and the known outcomes, we can construct a decision tree that can represent the probability of an event occurring.
The following table shows a very simple example of how decision trees can be generated:
| Car make | Year | Price |
| BMW | 2015 | >$40K |
| BMW | 2018 | >$40K |
| Honda | 2015 | <$40K |
| Honda | 2018 | >$40K |
| Nissan | 2015 | <$40K |
| Nissan | 2018 |
>$40K |
This is a very simple dataset that is represented by the following decision tree:
The aim of the machine learning algorithm is to generate decision trees that best represent the observations in the...
