The No Information Rate is the proportion of the largest class in the actual outcomes. It represents the accuracy that a naive model would achieve by always predicting the most frequent class. It's a baseline measure for classification performance.
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.
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.
Examples
cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth, threshold = 0.5, poslabel = 1)
nir <- dx_nir(cm)
print(nir)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <lgl> <lgl> <chr> <chr> <chr>
#> 1 No Information R… 0.62 0.625 NA NA 163/261 "" ""