Calculates Specificity, also known as the True Negative Rate (TNR), which is the proportion of actual negatives that are correctly identified as such by the classifier. Specificity is a key measure in evaluating the effectiveness of a classifier in identifying negative instances.
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
Specificity or TNR measures how well the classifier can identify negative instances, which is critical in situations where false positives carry a high cost. A higher specificity indicates a better performance in recognizing negative instances and avoiding false alarms.
The formula for Specificity is: $$Specificity = \frac{True Negatives}{True Negatives + False Positives}$$
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_specificity <- dx_specificity(cm, detail = "simple")
detailed_specificity <- dx_specificity(cm)
print(simple_specificity)
#> [1] 0.9202454
print(detailed_specificity)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 Specificity 92.0% (86.7%… 0.920 0.867 0.957 150/163 Binomial… ""