fpr: Calculate False Positive Rate (FPR)

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Calculate False Positive Rate (FPR)

Description

Calculates the False Positive Rate (FPR), which is the proportion of actual negatives that were incorrectly identified as positives by the classifier. FPR is also known as the fall-out rate and is crucial in evaluating the specificity of a classifier.

Usage

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

dx_fall_out(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

FPR is particularly important in contexts where false alarms are costly. It is used alongside True Negative Rate (specificity) to understand the classifier's ability to correctly identify negative instances. A lower FPR indicates a classifier that is better at correctly identifying negatives and not alarming false positives.

The formula for FPR is:

FPR = \frac{False Positives}{False Positives + True Negatives}

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_fpr <- dx_fpr(cm, detail = "simple")
detailed_fpr <- dx_fpr(cm)
print(simple_fpr)
print(detailed_fpr)

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