A random forest combines many decision trees, each trained with some randomness, and uses their joint answer as the prediction.
In plain words
One decision tree can be unreliable. A random forest trains many trees, each on a random sample of the training data and choosing among only some of the features, picked at random, when it splits. Because the trees differ, their combined answer is usually steadier than any single tree.
A quick example
To predict house prices, a random forest trains hundreds of trees, each on a different random sample of past sales. For a new house, every tree gives its own estimate and the forest combines them into one prediction.
Where you'll meet it
- Prediction on table-shaped data, where each example is a row of features.
- A common step up from a single decision tree.
Related
Sources
- DocsRandom forests, Google Decision Forests course · read 29 Sept 2026
- DocsEnsembles: gradient boosting, random forests, bagging, voting, stacking, scikit-learn · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.