Calculates the Fowlkes-Mallows Index (FM Index) for the provided confusion matrix. FM Index is the geometric mean of precision and recall, providing a balance measure between these two metrics.
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
Fowlkes-Mallows Index is defined as the geometric mean of the precision (Positive Predictive Value) and recall (True Positive Rate or Sensitivity). It's a useful measure when you want a balance between precision and recall without the harshness of the harmonic mean used in F1 score. A higher Fowlkes-Mallows Index indicates better precision and recall balance.
The formula for Fowlkes-Mallows Index is: $$FM = \sqrt{Precision \times Recall}$$
See also
dx_cm() to understand how to create and interact with a 'dx_cm' object.
dx_ppv(), dx_sensitivity() for components of FM Index.
Examples
cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth, threshold = 0.5, poslabel = 1)
simple_fm_index <- dx_fowlkes_mallows(cm, detail = "simple")
detailed_fm_index <- dx_fowlkes_mallows(cm)
print(simple_fm_index)
#> [1] 0.7632264
print(detailed_fm_index)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 Fowlkes-Mallows … 0.76 0.763 NA NA "" NA Spec…