These functions calculate the
sens() (sensitivity) of a measurement system
compared to a reference result (the "truth" or gold standard).
Highly related functions are
1 2 3 4 5 6 7 8 9 10
Not currently used.
The column identifier for the true class results
(that is a
The column identifier for the predicted class
results (that is also
The sensitivity (
sens()) is defined as the proportion of positive
results out of the number of samples which were actually
When the denominator of the calculation is
0, sensitivity is undefined.
This happens when both
# true_positive = 0 and
# false_negative = 0
are true, which mean that there were no true events. When computing binary
NA value will be returned with a warning. When computing
multiclass sensitivity, the individual
NA values will be removed, and the
computation will procede, with a warning.
tibble with columns
.estimate and 1 row of values.
For grouped data frames, the number of rows returned will be the same as the number of groups.
sens_vec(), a single
numeric value (or
There is no common convention on which factor level should
automatically be considered the "event" or "positive" result.
yardstick, the default is to use the first level. To
change this, a global option called
TRUE when the package is loaded. This can be changed
FALSE if the last level of the factor is considered the
level of interest by running:
options(yardstick.event_first = FALSE).
For multiclass extensions involving one-vs-all
comparisons (such as macro averaging), this option is ignored and
the "one" level is always the relevant result.
Macro, micro, and macro-weighted averaging is available for this metric.
The default is to select macro averaging if a
truth factor with more
than 2 levels is provided. Otherwise, a standard binary calculation is done.
vignette("multiclass", "yardstick") for more information.
Suppose a 2x2 table with notation:
The formulas used here are:
Sensitivity = A/(A+C)
Specificity = D/(B+D)
Prevalence = (A+C)/(A+B+C+D)
PPV = (Sensitivity * Prevalence) / ((Sensitivity * Prevalence) + ((1-Specificity) * (1-Prevalence)))
NPV = (Specificity * (1-Prevalence)) / (((1-Sensitivity) * Prevalence) + ((Specificity) * (1-Prevalence)))
See the references for discussions of the statistics.
Altman, D.G., Bland, J.M. (1994) “Diagnostic tests 1: sensitivity and specificity,” British Medical Journal, vol 308, 1552.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
# Two class data("two_class_example") sens(two_class_example, truth, predicted) # Multiclass library(dplyr) data(hpc_cv) hpc_cv %>% filter(Resample == "Fold01") %>% sens(obs, pred) # Groups are respected hpc_cv %>% group_by(Resample) %>% sens(obs, pred) # Weighted macro averaging hpc_cv %>% group_by(Resample) %>% sens(obs, pred, estimator = "macro_weighted") # Vector version sens_vec(two_class_example$truth, two_class_example$predicted) # Making Class2 the "relevant" level options(yardstick.event_first = FALSE) sens_vec(two_class_example$truth, two_class_example$predicted) options(yardstick.event_first = TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.