View source: R/class-detection_prevalence.R
detection_prevalence | R Documentation |
Detection prevalence is defined as the number of predicted positive events (both true positive and false positive) divided by the total number of predictions.
detection_prevalence(data, ...)
## S3 method for class 'data.frame'
detection_prevalence(
data,
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)
detection_prevalence_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 |
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 detection_prevalence_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.
Max Kuhn
Other class metrics:
accuracy()
,
bal_accuracy()
,
f_meas()
,
j_index()
,
kap()
,
mcc()
,
npv()
,
ppv()
,
precision()
,
recall()
,
sens()
,
spec()
# Two class
data("two_class_example")
detection_prevalence(two_class_example, truth, predicted)
# Multiclass
library(dplyr)
data(hpc_cv)
hpc_cv %>%
filter(Resample == "Fold01") %>%
detection_prevalence(obs, pred)
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
detection_prevalence(obs, pred)
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
detection_prevalence(obs, pred, estimator = "macro_weighted")
# Vector version
detection_prevalence_vec(
two_class_example$truth,
two_class_example$predicted
)
# Making Class2 the "relevant" level
detection_prevalence_vec(
two_class_example$truth,
two_class_example$predicted,
event_level = "second"
)
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