In decision trees, we have input and corresponding output in the training data. A decision tree, like any tree, has leaves, branches, and nodes. Leaves are the end nodes like a yes or no. Nodes are where a decision is taken. A decision tree consists of rules that we use to formulate a decision on the prediction of a data point.

Every node of the decision tree represents a feature and every edge coming out of an internal node represents a possible value or a possible interval of values of the tree. Each leaf of the tree represents a label value of the tree.

As we learned in the previous lessons, data points have features and labels. A task of a decision tree is to predict the label value based on fixed rules. The rules come from observing patterns on the training data.

Let's consider an example of determining the label values

Suppose the following training dataset is given. Formulate rules that help you determine the label value:

Figure 5.1: Dataset to formulate...