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gbm (version 2.2.2)

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)

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)

Value

The requested performance measure.

Arguments

obs

Observed value.

pred

Predicted value.

x

Numeric vector.

y, y.f, f, w, group, max.rank

Used internally.

metric

What type of performance measure to compute.

Author

Stefan Schroedl

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.

References

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

See Also

gbm