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 thetruth
andestimate
arguments, or atable
/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, afactor
vector.- estimate
The column identifier for the predicted class results (that is also
factor
). As withtruth
this can be specified different ways but the primary method is to use an unquoted variable name. For_vec()
functions, afactor
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 onestimate
.- na_rm
A
logical
value indicating whetherNA
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,hardhat::importance_weights()
, orhardhat::frequency_weights()
.- event_level
A single string. Either
"first"
or"second"
to specify which level oftruth
to consider as the "event". This argument is only applicable whenestimator = "binary"
. The default uses an internal helper that defaults to"first"
.
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