Intermediate Topic 23 of 51 AI & Machine Learning
Random Forests
Ensembles of decision trees voting together.
How it works
Trains many decision trees on bootstrap samples with random feature subsets and averages their votes, cutting variance. Gives feature importances and out-of-bag error.
Where it is used
Strong tabular baselines.
Common pitfall
Less interpretable and larger than a single tree.
Where it fits in the course
Random Forests is topic 23 of 51 in AI & Machine Learning, rated Intermediate. It follows Support Vector Machines. The next topic is Gradient Boosting.
Practice and explore
- Open the interactive lesson, with a mini quiz.
- Take the AI & Machine Learning quiz.
How to study this topic
- Read the explanation above and restate the definition in your own words.
- Open the interactive lesson and answer its mini quiz.
- Read the common pitfall, then write a short example that avoids it.
- Check your understanding in the AI & Machine Learning quiz, and revisit the topic if you miss a question.
Frequently asked questions
What is Random Forests?
Ensembles of decision trees voting together. It belongs to AI & Machine Learning and is an intermediate-level topic.
Where do people use Random Forests?
Strong tabular baselines.
What is the most common mistake with Random Forests?
Less interpretable and larger than a single tree.
What should I learn before this topic?
Start with Support Vector Machines, 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.
Is Random Forests hard to learn?
It is an Intermediate topic. Be comfortable with the Beginner topics in this area first, then read the common pitfall carefully, since it is where most mistakes happen.
How long does it take to learn Random Forests?
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 Random Forests?
Support Vector Machines, Gradient Boosting, Naive Bayes are closely connected. The full list is in the related topics section of this page.
What should I study after Random Forests?
Continue with Gradient Boosting, the next topic in AI & Machine Learning.
Related topics
- Support Vector Machines (AI & Machine Learning)
- Gradient Boosting (AI & Machine Learning)
- Naive Bayes (AI & Machine Learning)
- Feature Engineering (AI & Machine Learning)
- Random Variables (Math for Computing)