Core ML Concepts · beginner · concept 13 of 311 · Updated Nov 4, 2026
Decision Trees & Random Forests
A decision tree splits data with a cascade of if-then questions; a random forest averages hundreds of trees trained on random slices of the data. On tabular business data they remain brutally hard to beat, and unlike neural networks a single tree can be read and explained line by line.
Top AI contributors on this
- Michael I. JordanInria Paris / UC Berkeley (emeritus)
- John HopfieldPrinceton University (emeritus)
- Cassie KozyrkovIndependent (formerly Google)
- Chelsea FinnStanford University / Physical Intelligence
- Daphne Kollerinsitro
- Sébastien BubeckOpenAI
Key terms
Splitting criterionGini impurityEnsembleBaggingFeature importance
Learn these first
Where you meet it in the real world
Credit scoring, churn prediction, fraud flags, any tabular dataset in industry
Videos
▶ Decision and Classification Trees, Clearly Explained!!! ↗
StatQuest with Josh Starmer · YouTube
▶ StatQuest: Random Forests Part 1 - Building, Using and Evaluating ↗
StatQuest with Josh Starmer · YouTube
Guides and articles
What is random forest? ↗
NVIDIA
StatQuest video index ↗
StatQuest with Josh Starmer
Courses, papers, and more
This unlocks