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Beginner Topic 10 of 51 AI & Machine Learning

Decision Trees

Models splitting data by questions into a tree of decisions.

How it works

A decision tree predicts by asking a sequence of questions about features, splitting data at each internal node and giving a prediction at each leaf. The algorithm picks splits that maximize purity, measured with Gini impurity or entropy and information gain for classification, or variance reduction for regression, and grows recursively until a stopping rule is reached. Trees handle mixed feature types, need little preprocessing and are easy to interpret by following paths.

Where it is used

Credit decisions, medical triage rules, customer churn analysis and as the building block of random forests and gradient boosting use trees.

Common pitfall

Unconstrained trees overfit by memorizing training data, and small data changes can alter the tree structure. They create axis-aligned splits that struggle with some patterns and are biased toward features with many levels. Control depth, minimum leaf size and use pruning, or move to ensembles for accuracy.

Where it fits in the course

Decision Trees is topic 10 of 51 in AI & Machine Learning, rated Beginner. It follows Logistic Regression. The next topic is k-Nearest Neighbors.

Practice and explore

How to study this topic

  1. Read the explanation above and restate the definition in your own words.
  2. Open the interactive lesson and answer its mini quiz.
  3. Read the common pitfall, then write a short example that avoids it.
  4. Check your understanding in the AI & Machine Learning quiz, and revisit the topic if you miss a question.

Frequently asked questions

What is Decision Trees?
Models splitting data by questions into a tree of decisions. It belongs to AI & Machine Learning and is a beginner-level topic.
Where do people use Decision Trees?
Credit decisions, medical triage rules, customer churn analysis and as the building block of random forests and gradient boosting use trees.
What is the most common mistake with Decision Trees?
Unconstrained trees overfit by memorizing training data, and small data changes can alter the tree structure.
What should I learn before this topic?
Start with Logistic Regression, the topic just before it in AI & Machine Learning.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the AI & Machine Learning quiz. You can also explore Binary Search Tree in the Visual Lab.
Is Decision Trees hard to learn?
It is a Beginner topic, so it works as an entry point. If the terms are new, read the explanation twice and try the visualizer before the quiz.
How long does it take to learn Decision Trees?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to Decision Trees?
Logistic Regression, k-Nearest Neighbors, Linear Regression are closely connected. The full list is in the related topics section of this page.
What should I study after Decision Trees?
Continue with k-Nearest Neighbors, the next topic in AI & Machine Learning.

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