Concept
Ensembling
Ensembling is combining the predictions of multiple models (or multiple variants of one model) so the aggregate outperforms any single member. The intuition: independent models make uncorrelated errors, and averaging cancels those errors while preserving the shared signal.
The rest of “Ensembling” is a premium feature: every concept in the library gets a precise, practitioner-focused write-up like this one, cross-linked straight from the paper summaries that use it.
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