For many real-life problems, we can build a Decision Tree. In a Decision Tree, we ask a question (decision node), and based on the answer, we either come up with a decision (leaf node) or ask more questions (decision node), and so on.

Here is an example of a duck/not a duck test:

Applying the same logic to the training data allows us to derive one of the most important machine learning algorithms, which can be used for both regression and classification tasks. In this course, we will focus on classification.

The following video illustrates how it works:

With each decision node, we aim to split the training data so that the data points of each class are separated into their own leaf nodes.

A Decision Tree also handles multiclass classification with ease:

And classification with multiple features can also be handled by the decision tree. Now each decision node can split the data using any of the features.