apk: Average Precision at k

In Metrics: Evaluation Metrics for Machine Learning

Description

apk computes the average precision at k, in the context of information retrieval problems.

Usage

apk(k, actual, predicted)

Arguments

k	The number of elements of predicted to consider in the calculation.
actual	The ground truth vector of relevant documents. The vector can contain any numeric or character values, order does not matter, and the vector does not need to be the same length as predicted.
predicted	The predicted vector of retrieved documents. The vector can contain any numeric of character values. However, unlike actual, order does matter, with the most documents deemed most likely to be relevant at the beginning.

Details

apk loops over the first k values of predicted. For each value, if the value is contained within actual and has not been predicted before, we increment the number of sucesses by one and increment our score by the number of successes divided by k. Then, we return our final score divided by the number of relevant documents (i.e. the length of actual).

apk will return NaN if length(actual) equals 0.

See Also

apk f1

Examples

actual <- c('a', 'b', 'd')
predicted <- c('b', 'c', 'a', 'e', 'f')
apk(3, actual, predicted)

Example output

[1] 0.5555556

Metrics documentation built on May 1, 2019, 10:11 p.m.