gbm_roc_area: Compute Information Retrieval measures.

In gbm-developers/gbm3: Generalized Boosted Regression Models

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)

Arguments

obs	Observed value
pred	Predicted 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<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.

gbm-developers/gbm3 documentation built on April 28, 2024, 10:04 p.m.