It estimates the Geometric Mean score for a nominal/categorical predicted-observed dataset.

## Arguments

- data
(Optional) argument to call an existing data frame containing the data.

- obs
Vector with observed values (character | factor).

- pred
Vector with predicted values (character | factor).

- pos_level
Integer, for binary cases, indicating the order (1|2) of the level corresponding to the positive. Generally, the positive level is the second (2) since following an alpha-numeric order, the most common pairs are

`(Negative | Positive)`

,`(0 | 1)`

,`(FALSE | TRUE)`

. Default : 2.- atom
Logical operator (TRUE/FALSE) to decide if the estimate is made for each class (atom = TRUE) or at a global level (atom = FALSE); Default : FALSE. When dataset is "binomial" atom does not apply.

- tidy
Logical operator (TRUE/FALSE) to decide the type of return. TRUE returns a data.frame, FALSE returns a list; Default : FALSE.

- na.rm
Logic argument to remove rows with missing values (NA). Default is na.rm = TRUE.

## Value

an object of class `numeric`

within a `list`

(if tidy = FALSE) or within a
`data frame`

(if tidy = TRUE).

## Details

The gmean is a metric especially useful for imbalanced classes because it measures the balance between the classification performance on both major (over-represented) as well as on minor (under-represented) classes. As stated above, it is particularly useful when the number of observations belonging to each class is uneven.

The gmean score is equivalent to the square-root of the product of specificity and recall (a.k.a. sensitivity).

\(gmean = \sqrt{recall * specificity} \)

It is bounded between 0 and 1. The closer to 1 the better the classification performance, while zero represents the worst.

For the formula and more details, see online-documentation

## References

De Diego, I.M., Redondo, A.R., Fernández, R.R., Navarro, J., Moguerza, J.M. (2022). General Performance Score for classification problems. _ Appl. Intell. (2022)._ doi:10.1007/s10489-021-03041-7

## Examples

```
# \donttest{
set.seed(123)
# Two-class
binomial_case <- data.frame(labels = sample(c("True","False"), 100, replace = TRUE),
predictions = sample(c("True","False"), 100, replace = TRUE))
# Get gmean estimate for two-class case
gmean(data = binomial_case, obs = labels, pred = predictions)
#> $gmean
#> [1] 0.4939454
#>
# }
```