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
#>
# }