Decision Tree is a machine learning model that makes predictions by following a series of yes/no questions arranged like a flowchart, leading from the input to a final decision.
What it means in plain English
A decision tree splits data by asking a sequence of simple questions, each narrowing down the possibilities, until it reaches a conclusion at a “leaf.” Its great strength is that it is easy to understand — you can literally read the path of questions that led to any prediction. This transparency makes decision trees popular where explaining a decision matters.
On their own they can be limited, but combined in groups they become very powerful.
A simple example
A simple decision tree for approving a loan might ask: “Is income above a threshold? If yes, is credit history good? If yes, approve.” You can trace exactly why any applicant was approved or declined.
Why it matters
Decision trees are valued for being interpretable, and they are the building block of powerful ensemble methods like random forests and gradient boosting. Understanding them is a gateway to some of the most effective techniques for structured data.
Related terms
- Random Forest — many decision trees combined.
- Gradient Boosting — another way of combining trees.
- Explainability — a strength of decision trees.
Frequently asked questions
How does a decision tree work?
It splits data by asking a series of yes/no questions about the features, branching until it reaches a prediction — much like a flowchart of decisions.
Why are decision trees popular?
They are easy to interpret and visualise, handle both numbers and categories, and form the building blocks of powerful methods like random forests and gradient boosting.
