accuracy: Accuracy

In yardstick: Tidy Characterizations of Model Performance

Accuracy

Description

Accuracy is the proportion of the data that are predicted correctly.

Usage

accuracy(data, ...)

## S3 method for class 'data.frame'
accuracy(data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)

accuracy_vec(truth, estimate, na_rm = TRUE, case_weights = NULL, ...)

Arguments

data	Either a data.frame containing the columns specified by the truth and estimate arguments, or a table/matrix where the true class results should be in the columns of the table.
...	Not currently used.
truth	The column identifier for the true class results (that is a factor). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For ⁠_vec()⁠ functions, a factor vector.
estimate	The column identifier for the predicted class results (that is also factor). As with truth this can be specified different ways but the primary method is to use an unquoted variable name. For ⁠_vec()⁠ functions, a factor vector.
na_rm	A logical value indicating whether NA values should be stripped before the computation proceeds.
case_weights	The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For ⁠_vec()⁠ functions, a numeric vector, hardhat::importance_weights(), or hardhat::frequency_weights().

Details

Suppose a 2x2 table with notation:

	Reference
Predicted	Positive	Negative
Positive	A	B
Negative	C	D

The formula used here is:

\text{Accuracy} = \frac{A + D}{A + B + C + D}

Accuracy is a metric that should be maximized. The output ranges from 0 to 1, with 1 indicating perfect predictions.

Value

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 accuracy_vec(), a single numeric value (or NA).

Multiclass

Accuracy extends naturally to multiclass scenarios. Because of this, macro and micro averaging are not implemented.

Author(s)

Max Kuhn

See Also

All class metrics

Other class metrics: bal_accuracy(), detection_prevalence(), f_meas(), fall_out(), j_index(), kap(), markedness(), mcc(), miss_rate(), npv(), ppv(), precision(), recall(), roc_dist(), sedi(), sens(), spec()

Examples

library(dplyr)
data("two_class_example")
data("hpc_cv")

# Two class
accuracy(two_class_example, truth, predicted)

# Multiclass
# accuracy() has a natural multiclass extension
hpc_cv |>
  filter(Resample == "Fold01") |>
  accuracy(obs, pred)

# Groups are respected
hpc_cv |>
  group_by(Resample) |>
  accuracy(obs, pred)

yardstick documentation built on April 8, 2026, 1:06 a.m.