Glossary term
Glossary term
Foundations
A tactic for training a decision forest in which each decision tree considers only a random subset of possible features when learning the condition. Generally, a different subset of features is sampled for each node. In contrast, when training a decision tree without attribute sampling, all possible features are considered for each node.
Created for this library
A propensity-to-buy team uses attribute sampling in its random forest so each tree sees only a subset of features, reducing correlation between trees.
A medical research team applies attribute sampling when training a random forest on genomic data so no single SNP dominates every tree.
A risk modeling team relies on attribute sampling to keep trees in a credit forest diverse despite a handful of dominant predictive features.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License