In this section, you'll learn about another ML algorithm that's very popular and fast—decision trees. In decision trees, we build a tree-like structure of decisions; we start with the root, choose a feature and split into branches, and continue till we reach the leaves, which represent the predicted class or value. The algorithm of decision trees involves two main steps:
- Decide which features to choose and what conditions to use for splitting
- Know when to stop
Let's understand it with an example. Consider a sample of 40 students; we have three variables: the gender (boy or girl; discrete), class (XI or XII; discrete), and height (5 to 6 feet; continuous). Eighteen students prefer to go to the library in their spare time and rest prefer to play. We can build a decision tree to predict who will be going to the library and who will be going...
