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.
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
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: $$FDR = \frac{False Positives}{False Positives + True Positives}$$
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)
#> [1] 0.1604938
print(detailed_fdr)
#> # 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 Discovery … 16.0% … 0.160 0.0883 0.259 13/81 Binomial… ""