Computes average precision (AP) for binary probabilistic predictions.
Usage
averagePrecision(x, pred = NULL)
Arguments
- x
either a numeric vector of observed binary outcomes (0/1) when
pred is supplied, or a fitted model object from which both response
and predictions are extracted
- pred
numeric vector of predicted probabilities or scores. Required
when x is a response vector; ignored when x is a model
object.
Value
A numeric scalar containing the average precision score.
Details
Average precision summarizes the precision-recall curve as
$$AP = \sum_n (R_n - R_{n-1}) P_n,$$
where \(P_n\) and \(R_n\) are precision and recall at the
\(n\)-th distinct prediction threshold. Unlike metrics based on
predicted class labels, average precision does not require a classification
cutoff. Prediction values therefore need only be numeric scores; they are
not restricted to the interval [0, 1].
Examples
resp <- c(0, 0, 1, 1)
pred <- c(0.1, 0.4, 0.35, 0.8)
averagePrecision(resp, pred)
#> [1] 0.8333333
