Metric types
In yardstick: Tidy Characterizations of Model Performance

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Metric types

There are three main metric types in yardstick: class, class probability, and numeric. Each type of metric has standardized argument syntax, and all metrics return the same kind of output (a tibble with 3 columns). This standardization allows metrics to easily be grouped together and used with grouped data frames for computing on multiple resamples at once. Below are the five types of metrics, along with the types of the inputs they take.

1) Class metrics (hard predictions)

- `truth` - factor

- `estimate` - factor

2) Class probability metrics (soft predictions) - truth - factor

- `estimate / ...` - multiple numeric columns containing class probabilities

3) Numeric metrics

- `truth` - numeric

- `estimate` - numeric

4) Static survival metircs

- `truth` - Surv

- `estimate` - numeric

5) dynamic survival metrics

- `truth` - Surv

- `...` - list of data.frames, each containing the 3 columns `.eval_time`, `.pred_survival, and `.weight_censored`

Example

In the following example, the hpc_cv data set is used. It contains class probabilities and class predictions for a linear discriminant analysis fit to the HPC data set of Kuhn and Johnson (2013). It is fit with 10 fold cross-validation, and the predictions for all folds are included.

library(yardstick)
library(dplyr)
data("hpc_cv")

hpc_cv %>%
  group_by(Resample) %>%
  slice(1:3)

1 metric, 1 resample

hpc_cv %>%
  filter(Resample == "Fold01") %>%
  accuracy(obs, pred)

1 metric, 10 resamples

hpc_cv %>%
  group_by(Resample) %>%
  accuracy(obs, pred)

2 metrics, 10 resamples

class_metrics <- metric_set(accuracy, kap)

hpc_cv %>%
  group_by(Resample) %>%
  class_metrics(obs, estimate = pred)

Metrics

Below is a table of all of the metrics available in yardstick, grouped by type.

library(knitr)
library(dplyr)

yardns <- asNamespace("yardstick")
fns <- lapply(names(yardns), get, envir = yardns)
names(fns) <- names(yardns)

get_metrics <- function(fns, type) {
  where <- vapply(fns, inherits, what = type, FUN.VALUE = logical(1))
  paste0("`", sort(names(fns[where])), "()`")
}

all_metrics <- bind_rows(
  tibble(type = "class", metric = get_metrics(fns, "class_metric")),
  tibble(type = "class prob", metric = get_metrics(fns, "prob_metric")),
  tibble(type = "numeric", metric = get_metrics(fns, "numeric_metric")),
  tibble(type = "dynamic survival", metric = get_metrics(fns, "dynamic_survival_metric")),
  tibble(type = "static survival", metric = get_metrics(fns, "static_survival_metric"))
)

kable(all_metrics, format = "html")