recall | R Documentation |
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(),
...
)
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 |
case_weights |
The optional column identifier for case weights.
This should be an unquoted column name that evaluates to a numeric column
in |
event_level |
A single string. Either |
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.
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
).
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.
Max Kuhn
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()
Other relevance metrics:
f_meas()
,
precision()
# Two class
data("two_class_example")
recall(two_class_example, truth, predicted)
# Multiclass
library(dplyr)
data(hpc_cv)
hpc_cv %>%
filter(Resample == "Fold01") %>%
recall(obs, pred)
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
recall(obs, pred)
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
recall(obs, pred, estimator = "macro_weighted")
# Vector version
recall_vec(
two_class_example$truth,
two_class_example$predicted
)
# Making Class2 the "relevant" level
recall_vec(
two_class_example$truth,
two_class_example$predicted,
event_level = "second"
)
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