Calculates Detection Prevalence, which is the proportion of cases that are predicted positive by the classifier over the total number of cases. Detection Prevalence provides a measure of how often the condition is identified by the model, regardless of its actual prevalence.
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
Detection Prevalence is a measure of the frequency with which a classifier predicts a condition as positive. It is different from the actual condition prevalence in the population and is influenced by the classifier's threshold and performance characteristics. High detection prevalence could indicate a tendency of the model to predict more positive cases, which might be useful or detrimental depending on the specific application and the cost of false positives. It is important to compare Detection Prevalence with the actual condition prevalence to assess the model's performance.
The formula for Detection Prevalence is: $$Detection Prevalence = \frac{Number of Predicted Positives}{Total Number of Cases}$$
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_detection_prevalence <- dx_detection_prevalence(cm, detail = "simple")
detailed_detection_prevalence <- dx_detection_prevalence(cm)
print(simple_detection_prevalence)
#> [1] 0.3103448
print(detailed_detection_prevalence)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 Detection Preval… 31.0% … 0.310 0.255 0.370 81/261 Binomial… ""