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)
The requested performance measure.
Observed value.
Predicted value.
Numeric vector.
Used internally.
What type of performance measure to compute.
Stefan Schroedl
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.
gbm