gbm.roc.area: Compute Information Retrieval measures.

In DexGroves/gbm-lrd: Generalized Boosted Regression Models

Description

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.

Usage

gbm.roc.area(obs, pred)
ir.measure.conc(y.f, max.rank)
ir.measure.auc(y.f, max.rank)
ir.measure.mrr(y.f, max.rank)
ir.measure.map(y.f, max.rank)
ir.measure.ndcg(y.f, max.rank)
perf.pairwise(y, f, group, metric="ndcg", w=NULL, max.rank=0)

Arguments

obs	Observed value
pred	Predicted value
metric	What type of performance measure to compute.
y, y.f, f, w, group, max.rank	Used internally.

Details

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.

Value

The requested performance measure.

Author(s)

Stefan Schroedl

References

C. Burges (2010). "From RankNet to LambdaRank to LambdaMART: An Overview", Microsoft Research Technical Report MSR-TR-2010-82.

See Also

gbm

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

DexGroves/gbm-lrd documentation built on May 6, 2019, 1:35 p.m.