Author: Simon Couch
Date: 2026-04-07

# Grouping behavior in yardstick

#### Simon Couch

#### 2026-04-07

The 1.3.0 release of yardstick introduced an implementation for groupwise metrics. The use case motivating the implementation of this functionality is fairness metrics, though groupwise metrics have applications beyond that domain. Fairness metrics quantify the degree of disparity in a metric value across groups. To learn more about carrying out fairness-oriented analyses with tidymodels, see the blog post on the tidymodels website. This vignette will instead focus on groupwise metrics generally, clarifying the meaning of “groupwise” and demonstrating functionality with an example dataset.

    library(yardstick)
    library(dplyr)

    data("hpc_cv")

# Group-awareness

Even before the implementation of groupwise metrics, all yardstick metrics had been group-aware. When grouped data is passed to a group-aware metric, it will return metric values calculated for each group.

To demonstrate, we’ll make use of the `hpc_cv` data set, containing class probabilities and class predictions for a linear discriminant analysis fit to the HPC data set of Kuhn and Johnson (2013). The model is evaluated via 10-fold cross-validation, and the predictions for all folds are included.

    tibble(hpc_cv)
    #> # A tibble: 3,467 × 7
    #>    obs   pred     VF      F       M          L Resample
    #>    <fct> <fct> <dbl>  <dbl>   <dbl>      <dbl> <chr>
    #>  1 VF    VF    0.914 0.0779 0.00848 0.0000199  Fold01
    #>  2 VF    VF    0.938 0.0571 0.00482 0.0000101  Fold01
    #>  3 VF    VF    0.947 0.0495 0.00316 0.00000500 Fold01
    #>  4 VF    VF    0.929 0.0653 0.00579 0.0000156  Fold01
    #>  5 VF    VF    0.942 0.0543 0.00381 0.00000729 Fold01
    #>  6 VF    VF    0.951 0.0462 0.00272 0.00000384 Fold01
    #>  7 VF    VF    0.914 0.0782 0.00767 0.0000354  Fold01
    #>  8 VF    VF    0.918 0.0744 0.00726 0.0000157  Fold01
    #>  9 VF    VF    0.843 0.128  0.0296  0.000192   Fold01
    #> 10 VF    VF    0.920 0.0728 0.00703 0.0000147  Fold01
    #> # ℹ 3,457 more rows

For the purposes of this vignette, we’ll also add a column `batch` to the data and select off the columns for the class probabilities, which we don’t need.

    set.seed(1)

    hpc <-
      tibble(hpc_cv) |>
      mutate(batch = sample(c("a", "b"), nrow(hpc_cv), replace = TRUE)) |>
      select(-c(VF, F, M, L))

    hpc
    #> # A tibble: 3,467 × 4
    #>    obs   pred  Resample batch
    #>    <fct> <fct> <chr>    <chr>
    #>  1 VF    VF    Fold01   a
    #>  2 VF    VF    Fold01   b
    #>  3 VF    VF    Fold01   a
    #>  4 VF    VF    Fold01   a
    #>  5 VF    VF    Fold01   b
    #>  6 VF    VF    Fold01   a
    #>  7 VF    VF    Fold01   a
    #>  8 VF    VF    Fold01   a
    #>  9 VF    VF    Fold01   b
    #> 10 VF    VF    Fold01   b
    #> # ℹ 3,457 more rows

If we wanted to compute the accuracy of the first resampled model, we could write:

    hpc |>
      filter(Resample == "Fold01") |>
      accuracy(obs, pred)
    #> # A tibble: 1 × 3
    #>   .metric  .estimator .estimate
    #>   <chr>    <chr>          <dbl>
    #> 1 accuracy multiclass     0.726

The metric function returns one row, giving the `.metric`, `.estimator`, and `.estimate` for the whole data set it is passed.

If we instead group the data by fold, metric functions like `accuracy` will know to compute values for each group; in the output, each row will correspond to a Resample.

    hpc |>
      group_by(Resample) |>
      accuracy(obs, pred)
    #> # A tibble: 10 × 4
    #>    Resample .metric  .estimator .estimate
    #>    <chr>    <chr>    <chr>          <dbl>
    #>  1 Fold01   accuracy multiclass     0.726
    #>  2 Fold02   accuracy multiclass     0.712
    #>  3 Fold03   accuracy multiclass     0.758
    #>  4 Fold04   accuracy multiclass     0.712
    #>  5 Fold05   accuracy multiclass     0.712
    #>  6 Fold06   accuracy multiclass     0.697
    #>  7 Fold07   accuracy multiclass     0.675
    #>  8 Fold08   accuracy multiclass     0.721
    #>  9 Fold09   accuracy multiclass     0.673
    #> 10 Fold10   accuracy multiclass     0.699

Note that the first row, corresponding to `Fold01`, gives the same value as manually filtering for the observations corresponding to the first resample and then computing the accuracy.

This behavior is what we mean by group-awareness. When grouped data is passed to group-aware metric functions, they will return values for each group.

# Groupwise metrics

Groupwise metrics are associated with a data-column such that, when passed data with that column, the metric will temporarily group by that column, compute values for each of the groups defined by the column, and then aggregate the values computed for the temporary grouping back to the level of the input data’s grouping.

More concretely, let’s turn to an example where there is no pre-existing grouping in the data. Consider the portion of the HPC data pertaining to the first resample:

    hpc |>
      filter(Resample == "Fold01")
    #> # A tibble: 347 × 4
    #>    obs   pred  Resample batch
    #>    <fct> <fct> <chr>    <chr>
    #>  1 VF    VF    Fold01   a
    #>  2 VF    VF    Fold01   b
    #>  3 VF    VF    Fold01   a
    #>  4 VF    VF    Fold01   a
    #>  5 VF    VF    Fold01   b
    #>  6 VF    VF    Fold01   a
    #>  7 VF    VF    Fold01   a
    #>  8 VF    VF    Fold01   a
    #>  9 VF    VF    Fold01   b
    #> 10 VF    VF    Fold01   b
    #> # ℹ 337 more rows

Suppose that the `batch`es in the data represent two groups for which model performance ought not to differ. To quantify the degree to which model performance differs for these two groups, we could compute accuracy values for either group separately, and then take their difference. First, computing accuracies:

    acc_by_group <-
      hpc |>
      filter(Resample == "Fold01") |>
      group_by(batch) |>
      accuracy(obs, pred)

    acc_by_group
    #> # A tibble: 2 × 4
    #>   batch .metric  .estimator .estimate
    #>   <chr> <chr>    <chr>          <dbl>
    #> 1 a     accuracy multiclass     0.713
    #> 2 b     accuracy multiclass     0.739

Now, taking the difference:

    diff(c(acc_by_group$.estimate[2], acc_by_group$.estimate[1]))
    #> [1] -0.02518607

Groupwise metrics encode the `group_by()` and aggregation step (in this case, subtraction) shown above into a yardstick metric. We can define a new groupwise metric with the `new_groupwise_metric()` function:

    accuracy_diff <-
      new_groupwise_metric(
        fn = accuracy,
        name = "accuracy_diff",
        aggregate = function(acc_by_group) {
          diff(c(acc_by_group$.estimate[2], acc_by_group$.estimate[1]))
        }
      )

- The `fn` argument is the yardstick metric that will be computed for each data group.
- The `name` argument gives the name of the new metric we’ve created; we’ll call ours “accuracy difference.”
- The `aggregate` argument is a function defining how to go from `fn` output by group to a single numeric value.

The output, `accuracy_diff`, is a function subclass called a `metric_factory`:

    class(accuracy_diff)
    #> [1] "metric_factory" "function"

`accuracy_diff` now knows to take accuracy values for each group and then return the difference between the accuracy for the first and second result as output. The last thing we need to associate with the object is the name of the grouping variable to pass to `group_by()`; we can pass that variable name to `accuracy_diff` to do so:

    accuracy_diff_by_batch <- accuracy_diff(batch)

The output, `accuracy_diff_by_batch`, is a yardstick metric function like any other:

    class(accuracy)
    #> [1] "class_metric" "metric"       "function"

    class(accuracy_diff_by_batch)
    #> [1] "class_metric" "metric"       "function"

We can use the `accuracy_diff_by_batch()` metric in the same way that we would use `accuracy()`. On its own:

    hpc |>
      filter(Resample == "Fold01") |>
      accuracy_diff_by_batch(obs, pred)
    #> # A tibble: 1 × 4
    #>   .metric       .by   .estimator .estimate
    #>   <chr>         <chr> <chr>          <dbl>
    #> 1 accuracy_diff batch multiclass   -0.0252

We can also add `accuracy_diff_by_batch()` to metric sets:

    acc_ms <- metric_set(accuracy, accuracy_diff_by_batch)

    hpc |>
      filter(Resample == "Fold01") |>
      acc_ms(truth = obs, estimate = pred)
    #> # A tibble: 2 × 4
    #>   .metric       .estimator .estimate .by
    #>   <chr>         <chr>          <dbl> <chr>
    #> 1 accuracy      multiclass    0.726  <NA>
    #> 2 accuracy_diff multiclass   -0.0252 batch

Groupwise metrics are group-aware. When passed data with any grouping variables other than the column passed as the first argument to `accuracy_diff()`—in this case, `group`—`accuracy_diff_by_batch()` will behave like any other yardstick metric. For example:

    hpc |>
      group_by(Resample) |>
      accuracy_diff_by_batch(obs, pred)
    #> # A tibble: 10 × 5
    #>    Resample .metric       .by   .estimator .estimate
    #>    <chr>    <chr>         <chr> <chr>          <dbl>
    #>  1 Fold01   accuracy_diff batch multiclass -0.0252
    #>  2 Fold02   accuracy_diff batch multiclass  0.106
    #>  3 Fold03   accuracy_diff batch multiclass  0.0220
    #>  4 Fold04   accuracy_diff batch multiclass -0.000300
    #>  5 Fold05   accuracy_diff batch multiclass -0.0361
    #>  6 Fold06   accuracy_diff batch multiclass  0.0153
    #>  7 Fold07   accuracy_diff batch multiclass -0.0323
    #>  8 Fold08   accuracy_diff batch multiclass -0.0159
    #>  9 Fold09   accuracy_diff batch multiclass -0.0131
    #> 10 Fold10   accuracy_diff batch multiclass -0.0255

Groupwise metrics form the backend of fairness metrics in tidymodels. To learn more about groupwise metrics and their applications in fairness problems, see `new_groupwise_metric()`.