# accuracy

##### 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, ...)

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

```
# NOT RUN {
# Two class
data("two_class_example")
accuracy(two_class_example, truth, predicted)
# Multiclass
library(dplyr)
data(hpc_cv)
hpc_cv %>%
filter(Resample == "Fold01") %>%
accuracy(obs, pred)
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
accuracy(obs, pred)
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
accuracy(obs, pred, estimator = "macro_weighted")
# Vector version
accuracy_vec(two_class_example$truth, two_class_example$predicted)
# Making Class2 the "relevant" level
options(yardstick.event_first = FALSE)
accuracy_vec(two_class_example$truth, two_class_example$predicted)
options(yardstick.event_first = TRUE)
# }
```

*Documentation reproduced from package yardstick, version 0.0.4, License: GPL-2*