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A decision tree is a flowchart-like diagram that shows all possible outcomes of a series of decisions.

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We can think of a Decision Tree as a model that uses a tree-like structure to make decisions or predictions. It’s a supervised learning algorithm that can be used for Classification and Regression.

Mathematics

If you have studied Discrete Mathematics at some point you might have come across Decision Trees before.

It would look something like this

• We have a Root Node which is the starting point of the entire dataset
• Branches are lines that connect nodes, it shows the flow from one decision to another
• Internal Nodes are points where decisions are made based on input features
• Leaf Nodes are the terminal nodes at the end of branches that represent final outcomes / predictions

Classification of Decision Trees

1. Classification Tree
2. Regression Tree

Classification Tree

These are designed to predict categorical outcomes i.e predict labels/classes. This is based on the various features of the input eg: email spam detection

Regression Tree

These are used when the target variable is continuous i.e we need to predict numerical values rather than categories eg: house price detection based on factors such as size, location, and other features.