Calculates Sensitivity, also known as the True Positive Rate (TPR) or recall, which is the proportion of actual positives that are correctly identified as such by the classifier. Sensitivity is a key measure in evaluating the effectiveness of a classifier in identifying positive instances.
Usage
dx_sensitivity(cm, detail = "full", ...)
dx_recall(cm, detail = "full", ...)
dx_tpr(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
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
Sensitivity or TPR is an important measure in scenarios where missing a positive identification has serious consequences. It essentially measures the proportion of actual positives that are correctly identified, giving insight into the ability of the classifier to detect positive instances. A higher sensitivity indicates a better performance in recognizing positive instances.
The formula for Sensitivity is: $$Sensitivity = \frac{True Positives}{True Positives + False Negatives}$$
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_sensitivity <- dx_sensitivity(cm, detail = "simple")
detailed_sensitivity <- dx_sensitivity(cm)
print(simple_sensitivity)
#> [1] 0.6938776
print(detailed_sensitivity)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 Sensitivity 69.4% (59.3%… 0.694 0.593 0.783 68/98 Binomial… ""