dx_fdr: Calculate False Discovery Rate (FDR)

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Calculate False Discovery Rate (FDR)

Description

Calculates the False Discovery Rate (FDR), which is the proportion of false positives among all positive predictions. FDR is a critical measure in many classification contexts, particularly where the cost of a false positive is high.

Usage

dx_fdr(cm, detail = "full", ...)

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.
...	Additional arguments to pass to metric_binomial function, such as citype for type of confidence interval method.

Details

FDR is an important measure when the consequences of false discoveries (false positives) are significant. It helps in understanding the error rate among the positive predictions made by the classifier. A lower FDR indicates a better precision of the classifier in identifying only the true positives.

The formula for FDR is:

FDR = \frac{False Positives}{False Positives + True Positives}

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.

Examples

cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth,
  threshold =
    0.5, poslabel = 1
)
simple_fdr <- dx_fdr(cm, detail = "simple")
detailed_fdr <- dx_fdr(cm)
print(simple_fdr)
print(detailed_fdr)

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