Calculates Markedness for the provided confusion matrix. Markedness is a combined measure of PPV (Positive Predictive Value) and NPV (Negative Predictive Value). It reflects the effectiveness of a classifier in marking class labels correctly, ranging from -1 to 1.
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.
- boot
Logical specifying if confidence intervals should be generated via bootstrapping. Note, this can be slow.
- bootreps
The number of bootstrap replications for calculating confidence intervals.
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
Markedness is defined as Markedness = PPV + NPV - 1. It is the sum of the proportions
of predicted positives that are true positives (PPV) and the proportion of predicted negatives
that are true negatives (NPV) minus one. It's a useful measure when you want to consider both
the positive and negative predictive values of a test. A higher markedness indicates better performance.
The formula for Markedness is: $$Markedness = PPV + NPV - 1$$
Examples
cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth, threshold = 0.5, poslabel = 1)
simple_markedness <- dx_markedness(cm, detail = "simple")
detailed_markedness <- dx_markedness(cm)
print(simple_markedness)
#> [1] 0.6728395
print(detailed_markedness)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 Markedness 0.67 0.673 NA NA "" NA Specify `bo…