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()`

.

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

data | Either a |
---|---|

... | Not currently used. |

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

estimate | The column identifier for the predicted class
results (that is also |

estimator | One of: |

na_rm | A |

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`

).

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.

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

, the default is to use the *first* level. To
change this, a global option called `yardstick.event_first`

is
set to `TRUE`

when the package is loaded. This can be changed
to `FALSE`

if the *last* level of the factor is considered the
level of interest by running: `options(yardstick.event_first = FALSE)`

.
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.

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.

Suppose a 2x2 table with notation:

Reference | ||

Predicted | Relevant | Irrelevant |

Relevant | A | B |

Irrelevant | C | D |

The formulas used here are:

$$recall = A/(A+C)$$ $$precision = A/(A+B)$$ $$F_meas_\beta = (1+\beta^2) * precision * recall/((\beta^2 * precision)+recall)$$

See the references for discussions of the statistics.

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

Other class metrics:
`accuracy()`

,
`bal_accuracy()`

,
`detection_prevalence()`

,
`f_meas()`

,
`j_index()`

,
`kap()`

,
`mcc()`

,
`npv()`

,
`ppv()`

,
`precision()`

,
`sens()`

,
`spec()`

#> # A tibble: 1 x 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 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 recall macro 0.548#> # A tibble: 10 x 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 x 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 options(yardstick.event_first = FALSE) recall_vec(two_class_example$truth, two_class_example$predicted)#> [1] 0.7933884