Accuracy

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

Usage

accuracy(data, ...)

# S3 method for 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().

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.

See also

Other class metrics: bal_accuracy(), detection_prevalence(), f_meas(), j_index(), kap(), mcc(), npv(), ppv(), precision(), recall(), sens(), spec()

Author

Max Kuhn

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
data("two_class_example")
data("hpc_cv")

# Two class
accuracy(two_class_example, truth, predicted)
#> # A tibble: 1 × 3
#>   .metric  .estimator .estimate
#>   <chr>    <chr>          <dbl>
#> 1 accuracy binary         0.838

# Multiclass
# accuracy() has a natural multiclass extension
hpc_cv %>%
  filter(Resample == "Fold01") %>%
  accuracy(obs, pred)
#> # A tibble: 1 × 3
#>   .metric  .estimator .estimate
#>   <chr>    <chr>          <dbl>
#> 1 accuracy multiclass     0.726

# Groups are respected
hpc_cv %>%
  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