These functions calculate the `recall()`

of a measurement system for
finding relevant documents compared to reference results
(the truth regarding relevance). Highly related functions are `precision()`

and `f_meas()`

.

## Usage

```
recall(data, ...)
# S3 method for data.frame
recall(
data,
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)
recall_vec(
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)
```

## Arguments

- data
Either a

`data.frame`

containing the columns specified by the`truth`

and`estimate`

arguments, or a`table`

/`matrix`

where the true class results should be in the columns of the table.- ...
Not currently used.

- truth
The column identifier for the true class results (that is a

`factor`

). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For`_vec()`

functions, a`factor`

vector.- estimate
The column identifier for the predicted class results (that is also

`factor`

). As with`truth`

this can be specified different ways but the primary method is to use an unquoted variable name. For`_vec()`

functions, a`factor`

vector.- estimator
One of:

`"binary"`

,`"macro"`

,`"macro_weighted"`

, or`"micro"`

to specify the type of averaging to be done.`"binary"`

is only relevant for the two class case. The other three are general methods for calculating multiclass metrics. The default will automatically choose`"binary"`

or`"macro"`

based on`estimate`

.- na_rm
A

`logical`

value indicating whether`NA`

values should be stripped before the computation proceeds.- case_weights
The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in

`data`

. For`_vec()`

functions, a numeric vector.- event_level
A single string. Either

`"first"`

or`"second"`

to specify which level of`truth`

to consider as the "event". This argument is only applicable when`estimator = "binary"`

. The default uses an internal helper that generally defaults to`"first"`

, however, if the deprecated global option`yardstick.event_first`

is set, that will be used instead with a warning.

## Value

A `tibble`

with columns `.metric`

, `.estimator`

,
and `.estimate`

and 1 row of values.

For grouped data frames, the number of rows returned will be the same as the number of groups.

For `recall_vec()`

, a single `numeric`

value (or `NA`

).

## Details

The recall (aka sensitivity) is defined as the proportion of
relevant results out of the number of samples which were
actually relevant. When there are no relevant results, recall is
not defined and a value of `NA`

is returned.

When the denominator of the calculation is `0`

, recall is undefined. This
happens when both `# true_positive = 0`

and `# false_negative = 0`

are true,
which mean that there were no true events. When computing binary
recall, a `NA`

value will be returned with a warning. When computing
multiclass recall, the individual `NA`

values will be removed, and the
computation will procede, with a warning.

## Relevant Level

There is no common convention on which factor level should
automatically be considered the "event" or "positive" result
when computing binary classification metrics. In `yardstick`

, the default
is to use the *first* level. To alter this, change the argument
`event_level`

to `"second"`

to consider the *last* level of the factor the
level of interest. For multiclass extensions involving one-vs-all
comparisons (such as macro averaging), this option is ignored and
the "one" level is always the relevant result.

## Multiclass

Macro, micro, and macro-weighted averaging is available for this metric.
The default is to select macro averaging if a `truth`

factor with more
than 2 levels is provided. Otherwise, a standard binary calculation is done.
See `vignette("multiclass", "yardstick")`

for more information.

## Implementation

Suppose a 2x2 table with notation:

Reference | ||

Predicted | Relevant | Irrelevant |

Relevant | A | B |

Irrelevant | C | D |

The formulas used here are:

$$recall = A/(A+C)$$ $$precision = A/(A+B)$$ $$F_{meas} = (1+\beta^2) * precision * recall/((\beta^2 * precision)+recall)$$

See the references for discussions of the statistics.

## References

Buckland, M., & Gey, F. (1994). The relationship
between Recall and Precision. *Journal of the American Society
for Information Science*, 45(1), 12-19.

Powers, D. (2007). Evaluation: From Precision, Recall and F Factor to ROC, Informedness, Markedness and Correlation. Technical Report SIE-07-001, Flinders University

## Examples

```
# Two class
data("two_class_example")
recall(two_class_example, truth, predicted)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 recall binary 0.880
# Multiclass
library(dplyr)
data(hpc_cv)
hpc_cv %>%
filter(Resample == "Fold01") %>%
recall(obs, pred)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 recall macro 0.548
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
recall(obs, pred)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 recall macro 0.548
#> 2 Fold02 recall macro 0.541
#> 3 Fold03 recall macro 0.634
#> 4 Fold04 recall macro 0.570
#> 5 Fold05 recall macro 0.550
#> 6 Fold06 recall macro 0.540
#> 7 Fold07 recall macro 0.531
#> 8 Fold08 recall macro 0.584
#> 9 Fold09 recall macro 0.568
#> 10 Fold10 recall macro 0.537
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
recall(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 recall macro_weighted 0.726
#> 2 Fold02 recall macro_weighted 0.712
#> 3 Fold03 recall macro_weighted 0.758
#> 4 Fold04 recall macro_weighted 0.712
#> 5 Fold05 recall macro_weighted 0.712
#> 6 Fold06 recall macro_weighted 0.697
#> 7 Fold07 recall macro_weighted 0.675
#> 8 Fold08 recall macro_weighted 0.721
#> 9 Fold09 recall macro_weighted 0.673
#> 10 Fold10 recall macro_weighted 0.699
# Vector version
recall_vec(
two_class_example$truth,
two_class_example$predicted
)
#> [1] 0.879845
# Making Class2 the "relevant" level
recall_vec(
two_class_example$truth,
two_class_example$predicted,
event_level = "second"
)
#> [1] 0.7933884
```