numeric_metric_summarizer()
, class_metric_summarizer()
,
prob_metric_summarizer()
, curve_metric_summarizer()
,
dynamic_survival_metric_summarizer()
, and
static_survival_metric_summarizer()
are useful alongside check_metric and
yardstick_remove_missing for implementing new custom metrics. These
functions call the metric function inside dplyr::summarise()
or
dplyr::reframe()
for curve_metric_summarizer()
. See Custom performance metrics for more
information.
numeric_metric_summarizer(
name,
fn,
data,
truth,
estimate,
...,
na_rm = TRUE,
case_weights = NULL,
fn_options = list(),
error_call = caller_env()
)class_metric_summarizer(
name,
fn,
data,
truth,
estimate,
...,
estimator = NULL,
na_rm = TRUE,
event_level = NULL,
case_weights = NULL,
fn_options = list(),
error_call = caller_env()
)
prob_metric_summarizer(
name,
fn,
data,
truth,
...,
estimator = NULL,
na_rm = TRUE,
event_level = NULL,
case_weights = NULL,
fn_options = list(),
error_call = caller_env()
)
curve_metric_summarizer(
name,
fn,
data,
truth,
...,
estimator = NULL,
na_rm = TRUE,
event_level = NULL,
case_weights = NULL,
fn_options = list(),
error_call = caller_env()
)
dynamic_survival_metric_summarizer(
name,
fn,
data,
truth,
...,
na_rm = TRUE,
case_weights = NULL,
fn_options = list(),
error_call = caller_env()
)
static_survival_metric_summarizer(
name,
fn,
data,
truth,
estimate,
...,
na_rm = TRUE,
case_weights = NULL,
fn_options = list(),
error_call = caller_env()
)
curve_survival_metric_summarizer(
name,
fn,
data,
truth,
...,
na_rm = TRUE,
case_weights = NULL,
fn_options = list(),
error_call = caller_env()
)
A single character representing the name of the metric to
use in the tibble
output. This will be modified to include the type
of averaging if appropriate.
The vector version of your custom metric function. It
generally takes truth
, estimate
, na_rm
, and any other extra arguments
needed to calculate the metric.
The data frame with truth
and estimate
columns passed in from
the data frame version of your metric function that called
numeric_metric_summarizer()
, class_metric_summarizer()
,
prob_metric_summarizer()
, curve_metric_summarizer()
,
dynamic_survival_metric_summarizer()
, or
static_survival_metric_summarizer()
.
The unquoted column name corresponding to the truth
column.
Generally, the unquoted column name corresponding to
the estimate
column. For metrics that take multiple columns through ...
like class probability metrics, this is a result of dots_to_estimate()
.
These dots are for future extensions and must be empty.
A logical
value indicating whether NA
values should be
stripped before the computation proceeds. The removal is executed in
yardstick_remove_missing()
.
For metrics supporting case weights, an unquoted
column name corresponding to case weights can be passed here. If not NULL
,
the case weights will be passed on to fn
as the named argument
case_weights
.
A named list of metric specific options. These
are spliced into the metric function call using !!!
from rlang
. The
default results in nothing being spliced into the call.
The execution environment of a currently
running function, e.g. caller_env()
. The function will be
mentioned in error messages as the source of the error. See the
call
argument of abort()
for more information.
This can either be NULL
for the default auto-selection of
averaging ("binary"
or "macro"
), or a single character to pass along to
the metric implementation describing the kind of averaging to use.
This can either be NULL
to use the default event_level
value of the fn
or a single string of either "first"
or "second"
to pass along describing which level should be considered the "event".
numeric_metric_summarizer()
, class_metric_summarizer()
,
prob_metric_summarizer()
, curve_metric_summarizer()
,
dynamic_survival_metric_summarizer()
, and
dynamic_survival_metric_summarizer()
are generally called from the data
frame version of your metric function. It knows how to call your metric over
grouped data frames and returns a tibble
consistent with other metrics.
check_metric yardstick_remove_missing finalize_estimator()
dots_to_estimate()