# mapk: Mean Average Precision at k In Metrics: Evaluation Metrics for Machine Learning

## Description

`mapk` computes the mean average precision at k for a set of predictions, in the context of information retrieval problems.

## Usage

 `1` ```mapk(k, actual, predicted) ```

## Arguments

 `k` The number of elements of `predicted` to consider in the calculation. `actual` A list of vectors, where each vector represents a ground truth vector of relevant documents. In each vector, the elements can be numeric or character values, and the order of the elements does not matter. `predicted` A list of vectors, where each vector represents the predicted vector of retrieved documents for the corresponding element of `actual`. In each vector, the order of the elements does matter, with the elements believed most likely to be relevant at the beginning.

## Details

`mapk` evaluates `apk` for each pair of elements from `actual` and `predicted`.

`apk` `f1`

## Examples

 ```1 2 3 4 5 6 7``` ```actual <- list(c('a', 'b'), c('a'), c('x', 'y', 'b')) predicted <- list(c('a', 'c', 'd'), c('x', 'b', 'a', 'b'), c('y')) mapk(2, actual, predicted) actual <- list(c(1, 5, 7, 9), c(2, 3), c(2, 5, 6)) predicted <- list(c(5, 6, 7, 8, 9), c(1, 2, 3), c(2, 4, 6, 8)) mapk(3, actual, predicted) ```

### Example output

```[1] 0.3333333
[1] 0.5648148
```

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