In this chapter, we covered a very important and popular algorithm in machine learning called as decision trees. A decision tree is very similar to a flowchart and is based on a set of rules. A decision tree algorithm learns from a dataset and builds a set of rules. Based on these rules, it splits the dataset into two (in the case of binary splits) or more parts. When a new data is fed in for predictions based on the attributes of the data, a particular path is taken and this follows along the full path of rules in the tree until a particular response is reached.
There are many ways in which we can split data in a decision tree. We explored two of the most common ways called Entropy and Gini Impurity. In either of these cases, the main criteria is to use the split mechanism, which makes the split set as homogeneous as possible. Both Entropy and Gini Impurity are mathematical formulas or approaches and as such the entire model works on numerical data.
In the next chapter, we will learn...