R: Recall

recall {yardstick} | R Documentation

## Recall

### Description

These functions calculate the `recall()` of a measurement system for finding relevant documents compared to reference results (the truth regarding relevance). Highly related functions are `precision()` and `f_meas()`.

### Usage

    recall(data, ...)

    ## S3 method for class 'data.frame'
    recall(
      data,
      truth,
      estimate,
      estimator = NULL,
      na_rm = TRUE,
      event_level = yardstick_event_level(),
      ...
    )

    recall_vec(
      truth,
      estimate,
      estimator = NULL,
      na_rm = TRUE,
      event_level = yardstick_event_level(),
      ...
    )

### 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.
`estimator` | One of: `"binary"`, `"macro"`, `"macro_weighted"`, or `"micro"` to specify the type of averaging to be done. `"binary"` is only relevant for the two class case. The other three are general methods for calculating multiclass metrics. The default will automatically choose `"binary"` or `"macro"` based on `estimate`.
`na_rm` | A `logical` value indicating whether `NA` values should be stripped before the computation proceeds.
`event_level` | A single string. Either `"first"` or `"second"` to specify which level of `truth` to consider as the "event". This argument is only applicable when `estimator = "binary"`. The default uses an internal helper that generally defaults to `"first"`, however, if the deprecated global option `yardstick.event_first` is set, that will be used instead with a warning.

### Details

The recall (aka sensitivity) is defined as the proportion of relevant results out of the number of samples which were actually relevant. When there are no relevant results, recall is not defined and a value of `NA` is returned.

When the denominator of the calculation is `0`, recall is undefined. This happens when both `# true_positive = 0` and `# false_negative = 0` are true, which mean that there were no true events. When computing binary recall, a `NA` value will be returned with a warning. When computing multiclass recall, the individual `NA` values will be removed, and the computation will procede, with a warning.

### 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 `recall_vec()`, a single `numeric` value (or `NA`).

### Relevant Level

There is no common convention on which factor level should automatically be considered the "event" or "positive" result when computing binary classification metrics. In `yardstick`, the default is to use the first level. To alter this, change the argument `event_level` to `"second"` to consider the last level of the factor the level of interest. For multiclass extensions involving one-vs-all comparisons (such as macro averaging), this option is ignored and the "one" level is always the relevant result.

### Multiclass

Macro, micro, and macro-weighted averaging is available for this metric. The default is to select macro averaging if a `truth` factor with more than 2 levels is provided. Otherwise, a standard binary calculation is done. See `vignette("multiclass", "yardstick")` for more information.

### Implementation

Suppose a 2x2 table with notation:

| Reference |
Predicted | Relevant | Irrelevant
Relevant | A | B
Irrelevant | C | D

The formulas used here are:

recall = A/(A+C)

precision = A/(A+B)

F_{meas} = (1+β^2) * precision * recall/((β^2 * precision)+recall)

See the references for discussions of the statistics.

### Author(s)

Max Kuhn

### References

Buckland, M., & Gey, F. (1994). The relationship between Recall and Precision. Journal of the American Society for Information Science, 45(1), 12-19.

Powers, D. (2007). Evaluation: From Precision, Recall and F Factor to ROC, Informedness, Markedness and Correlation. Technical Report SIE-07-001, Flinders University

### See Also

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

Other relevance metrics: `f_meas()`, `precision()`

### Examples

    # Two class
    data("two_class_example")
    recall(two_class_example, truth, predicted)

    # Multiclass
    library(dplyr)
    data(hpc_cv)

    hpc_cv %>%
      filter(Resample == "Fold01") %>%
      recall(obs, pred)

    # Groups are respected
    hpc_cv %>%
      group_by(Resample) %>%
      recall(obs, pred)

    # Weighted macro averaging
    hpc_cv %>%
      group_by(Resample) %>%
      recall(obs, pred, estimator = "macro_weighted")

    # Vector version
    recall_vec(
      two_class_example$truth,
      two_class_example$predicted
    )

    # Making Class2 the "relevant" level
    recall_vec(
      two_class_example$truth,
      two_class_example$predicted,
      event_level = "second"
    )

[Package yardstick version 0.0.9 Index]