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

accuracy(data, ...)

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

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

Arguments

data

Either a data.frame containing the truth and estimate columns, 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.

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()

Examples

# Two class data("two_class_example") accuracy(two_class_example, truth, predicted)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 accuracy binary 0.838
# Multiclass 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(hpc_cv) hpc_cv %>% filter(Resample == "Fold01") %>% accuracy(obs, pred)
#> # A tibble: 1 x 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 x 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
# Weighted macro averaging hpc_cv %>% group_by(Resample) %>% accuracy(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 x 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
# Vector version accuracy_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.838
# Making Class2 the "relevant" level options(yardstick.event_first = FALSE) accuracy_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.838
options(yardstick.event_first = TRUE)