Calculates the Geometric Mean (G-mean) for the provided confusion matrix. G-mean is a measure of a model's performance that considers both the sensitivity (True Positive Rate) and specificity (True Negative Rate), especially useful in imbalanced datasets.
Arguments
- cm
A dx_cm object created by
dx_cm().- detail
Character specifying the level of detail in the output: "simple" for raw estimate, "full" for detailed estimate including 95% confidence intervals.
- boot
Logical specifying if confidence intervals should be generated via bootstrapping. Note, this can be slow.
- bootreps
The number of bootstrap replications for calculating confidence intervals.
Value
Depending on the detail parameter, returns a numeric value
representing the calculated metric or a data frame/tibble with
detailed diagnostics including confidence intervals and possibly other
metrics relevant to understanding the metric.
Details
G-mean is the geometric mean of sensitivity and specificity. It tries to maximize the accuracy on each of the two classes while keeping these accuracies balanced. For a classifier to achieve a high G-mean score, it must perform well on both positive and negative classes.
The formula for G-mean is: $$G-mean = \sqrt{Sensitivity \times Specificity}$$
See also
dx_cm() to understand how to create and interact with a 'dx_cm' object.
dx_sensitivity(), dx_specificity() for components of G-mean.
Examples
cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth, threshold = 0.5, poslabel = 1)
simple_g_mean <- dx_g_mean(cm, detail = "simple")
detailed_g_mean <- dx_g_mean(cm)
print(simple_g_mean)
#> [1] 0.7990855
print(detailed_g_mean)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 G-mean 0.8 0.799 NA NA "" NA Specify `boot …