R: Compute Information Retrieval measures.

gbm.roc.area {gbm} | R Documentation

## 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)

### 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.

### 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`

[Package gbm version 2.1.8 Index]