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.
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
citypefor type of confidence interval method.
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
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: $$FPR = \frac{False Positives}{False Positives + True Negatives}$$
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)
#> [1] 0.0797546
print(detailed_fpr)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 False Positive R… 8.0% (… 0.0798 0.0431 0.133 13/163 Binomial… ""