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 class '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(),
...
)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.
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.
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.
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.
A logical value indicating whether NA
values should be stripped before the computation proceeds.
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,
hardhat::importance_weights(), or hardhat::frequency_weights().
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 defaults to "first".
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
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.
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:
$$recall = A/(A+C)$$ $$precision = A/(A+B)$$ $$F_{meas} = (1+\beta^2) * precision * recall/((\beta^2 * precision)+recall)$$
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
# 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