metric_summarizer() is useful alongside metric_vec_template() for implementing new custom metrics. metric_summarizer() calls the metric function inside dplyr::summarise(). metric_vec_template() is a generalized function that calls the core implementation of a metric function, and includes a number of checks on the types, lengths, and argument inputs. See vignette("custom-metrics", "yardstick") for more information.

  estimator = NULL,
  na_rm = TRUE,
  metric_fn_options = list()



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 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().


For numeric metrics, this is left as NA so averaging is not passed on to the metric function implementation. For classification metrics, 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.


A logical value indicating whether NA values should be stripped before the computation proceeds. The removal is executed in metric_vec_template().


Currently not used. Metric specific options are passed in through metric_fn_options.


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.


metric_summarizer() is 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.

See also