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Random forest

A random forest is an ensemble of many decision trees, each trained on a different random subset of data and features, whose predictions are averaged or voted together.

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Prerequisite: Decision tree

What matters

Averaging many varied trees reduces the high variance any single decision tree tends to have, usually giving a more stable and accurate model than one tree alone.

Example

A random forest of 200 trees might each vote 'approve' or 'deny' on a loan application, and the majority vote becomes the forest's final prediction.

Common misconception

A random forest does not train one tree and copy it many times; each tree trains on a different random sample of data and features specifically to make them vary.

Recall question

Why does averaging many varied trees reduce variance compared to one tree alone?

Keywords and related concepts

random forestbaggingensembledecision tree