informedness | R Documentation |
Calculates Informedness for the provided confusion matrix. Informedness is a combined measure of Sensitivity (True Positive Rate) and Specificity (True Negative Rate). It reflects the probability that a classifier is informed about the true class, ranging from -1 to 1.
dx_informedness(cm, detail = "full", boot = FALSE, bootreps = 1000)
dx_youden_j(cm, detail = "full", boot = FALSE, bootreps = 1000)
cm |
A dx_cm object created by |
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. |
Informedness is defined as Informedness = Sensitivity + Specificity - 1
. It is the sum of the true positive rate
and the true negative rate minus one. It's a useful measure when you want to consider both
the sensitivity and specificity of a test. A higher informedness indicates better overall performance
of the classifier in distinguishing between the classes.
The formula for Informedness is:
Informedness = Sensitivity + Specificity - 1
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.
dx_cm()
to understand how to create and interact with a 'dx_cm' object.
dx_sensitivity()
, dx_specificity()
for components of informedness.
cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth, threshold = 0.5, poslabel = 1)
simple_informedness <- dx_informedness(cm, detail = "simple")
detailed_informedness <- dx_informedness(cm)
print(simple_informedness)
print(detailed_informedness)
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