R: Accuracy

accuracy {yardstick} | R Documentation

## 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, ...)

    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.

### Author(s)

Max Kuhn

### See Also

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

[Package yardstick version 0.0.8 Index]