Functions to compute Information Retrieval measures for pairwise loss for a single group. The function returns the respective metric, or a negative value if it is undefined for the given group.
gbm.roc.area(obs, pred) gbm.conc(x) ir.measure.conc(y.f, max.rank = 0) ir.measure.auc(y.f, max.rank = 0) ir.measure.mrr(y.f, max.rank) ir.measure.map(y.f, max.rank = 0) ir.measure.ndcg(y.f, max.rank) perf.pairwise(y, f, group, metric = "ndcg", w = NULL, max.rank = 0)
| obs | Observed value. |
|---|---|
| pred | Predicted value. |
| x | ?. |
| y, y.f, f, w, group, max.rank | Used internally. |
| metric | What type of performance measure to compute. |
The requested performance measure.
For simplicity, we have no special handling for ties; instead, we break ties randomly. This is slightly inaccurate for individual groups, but should have only a small effect on the overall measure.
gbm.conc computes the concordance index: Fraction of all pairs (i,j)
with i<j, x[i] != x[j], such that x[j] < x[i]
If obs is binary, then gbm.roc.area(obs, pred) =
gbm.conc(obs[order(-pred)]).
gbm.conc is more general as it allows non-binary targets, but is
significantly slower.
C. Burges (2010). "From RankNet to LambdaRank to LambdaMART: An Overview", Microsoft Research Technical Report MSR-TR-2010-82.
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets.