Decision trees are machine learning models that try to find patterns in the features of data points. Take a look at the tree on this page. This tree tries to predict whether a student will get an A on their next test.
By asking questions like “What is the student’s average grade in the class” the decision tree tries to get a better understanding of their chances on the next test.
In order to make a classification, this classifier needs a data point with four features:
- The student’s average grade in the class.
- The number of hours the student plans on studying for the test.
- The number of hours the student plans on sleeping the night before the test.
- Whether or not the student plans on cheating.
For example, let’s say that somebody has a “B” average in the class, studied for more than 3 hours, slept less than 5 hours before the test, and doesn’t plan to cheat. If we start at the top of the tree and take the correct path based on that data, we’ll arrive at a leaf node that predicts the person will not get an A on the next test.
In this course, you’ll learn how to create a tree like this!
Play around with the applet on this page. Fill in values at the top of the page and see which leaf (and therefore classification) the data point ends up at.