MENTAL MODEL · M12692

Random Forest

DecideHigh supportDecision Science

Included

Version 1.0.0 · Updated 2026-07-30

CORE DEFINITION

Build multiple decision trees, each trained on randomly sampled data and features, and the final result is determined by voting among all trees. Scaffold role: collective wisdom surpasses individual. The ensemble of multiple 'weak' models often outperforms a single 'strong' model, suitable for decision-making and prediction.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

Collective wisdom surpasses individual. The ensemble of multiple 'weak' models often outperforms a single 'strong' model, suitable for decision-making and prediction.

anchor

Anchor fast decisions

Random forest integrates multiple decision trees, using dual randomness of bootstrap sampling of samples and random feature selection at nodes to reduce overfitting and improve generalization.

MINIMUM ACTION

In progress 0/1

Practice this model in one real situation:

Bootstrap sampling to build multiple trees. At each node, randomly select features for splitting. Ensemble voting or averaging outputs.

Check to track your progress (stored locally)

Learning progress0%

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Source support: Explicit

link
en.wikipedia.orghttps://en.wikipedia.org/wiki/Random_forestZH · Explicit
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