Decision trees and random forests
What you will learn
Decision trees and random forests is an important step in your Machine Learning learning path. In this chapter you will understand the core idea, recognize when to use it, and practise explaining it in plain language.
Why it matters
AI projects succeed when the problem, inputs, outputs and evaluation criteria are clear. Before choosing a library or model, identify the task, the data available and what a useful result would look like. This principle applies to Decision trees and random forests too.
Worked example
Imagine building a student-support assistant. Define the user question, available reference material, expected answer and failure cases. Write down how Decision trees and random forests could improve the system, and what evidence would demonstrate that improvement.
Common mistakes
- Starting with tools before defining the problem.
- Testing only one happy-path example.
- Ignoring privacy, data quality, costs or incorrect outputs.
Knowledge check
- Explain the concept to a beginner in two sentences.
- Name one real-world use case and one limitation.
- Describe a simple way to test whether it works.
Practice: Write a short explanation of Decision trees and random forests, sketch a practical use case, and list three test cases. Share your notes with a peer.
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