dx_g_mean: Calculate G-mean

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Calculate G-mean

Description

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.

Usage

dx_g_mean(cm, detail = "full", boot = FALSE, bootreps = 1000)

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.

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}

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.

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)
print(detailed_g_mean)

overdodactyl/diagnosticSummary documentation built on Jan. 28, 2024, 10:07 a.m.