An ensemble learning algorithm that constructs a collection of decision trees during training and outputs the average prediction across all trees. Each tree is trained on a bootstrapped sample of the data with random feature selection at each split, which decorrelates the trees and reduces overall variance while preserving bias. The algorithm operates through bagging (bootstrap aggregation), random feature subspace selection, and majority voting (classification) or mean aggregation (regression). Persistence mechanism: trained model artifacts (serialized tree structures) stored as data files, executable by any runtime that implements the tree traversal and aggregation protocol. [formal: forestum casuum aleatorium | substrate: behavior | horizon: a moment | explicit: yes | epoch: 0.11]
Accepted ontology entry
random forest
An ensemble learning algorithm that constructs a collection of decision trees during training and outputs the average prediction across all trees. Each tree is trained on a bootstrapped sample of the data with random feature selection at e…
Definition
Why it is in scope
An ensemble machine learning method that constructs a multitude of decision trees at training time and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. It is a human-made algorithm that persists through software libraries, academic literature, and computational practice.
Names and aliases
- random foresten · CANONICAL
Relations from this entry
- cmrg0scos00ef2a1nklfvbk7xINSTANCE_OF →
Random forest is a specific kind of machine learning algorithm that uses ensemble methods. A competent speaker would call a random forest 'a type of machine learning.'
- cmsddehj003sp3vv3r6h06pb8INSTANCE_OF →
Random forest IS a specific kind of statistical method: an ensemble method that builds multiple decision trees on bootstrapped samples and aggregates their predictions. A competent speaker would call it 'a statistical method' or 'a machine learning method'.
Relations to this entry
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Record identity
- Created
- Aug 3, 2026, 2:01 PM UTC
- Content hash
- 6798c43e0a5d4d164ebba5a3a5386040e1608469d680ec41d0b3469c74fa5bdb