Ranking Objectives
Stub. Created as a placeholder — expand with vault-sourced content.
Definition
The objective (loss) a Learning to Rank model optimizes. Three families: pointwise (regress/classify each doc), pairwise (preference between two docs), listwise (optimize a list metric like NDCG directly). These map onto the evaluation paradigms in Pointwise Relevance Evaluation / Pairwise Relevance Evaluation / Listwise Relevance Evaluation.
Concrete objectives by library (to verify/expand)
- LightGBM:
lambdarank - XGBoost:
rank:ndcg,rank:map,rank:pairwise - CatBoost:
YetiRank,PairLogit,QueryRMSE
TODO
- Confirm objective lists against current library docs before treating as fact.
- Cross-link each to LambdaMART.