Popular ensemble methods

D Opitz, R. M. (1999). Popular ensemble methods: An empirical study.

Type

Unclassified

Intellectual base

Unclassified

Year

1999

DOI

10.1613/jair.614

Link

https://doi.org/10.1613/jair.614

Cited by

1 citation across 1 artifact.

Domain-specific

• Ensemble learning
  • Bagging, random forests, boosting, stacking and voting differ in dependence, training sequence and combiner; leakage-free validation and diversity matter more than model count alone.

Verification

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Registry ID ref:21af8336ef16 · see in the full table