gbm (version 2.1.1)

gbm.roc.area: Compute Information Retrieval measures.

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.

Value

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

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.

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.