Youden's J statistic is defined as:
spec() - 1
A related metric is Informedness, see the Details section for the relationship.
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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 value of the J-index ranges from [0, 1] and is
1 when there are
no false positives and no false negatives.
The binary version of J-index is equivalent to the binary concept of Informedness. Macro-weighted J-index is equivalent to multiclass informedness as defined in Powers, David M W (2011), equation (42).
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.
j_index_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.
Youden, W.J. (1950). "Index for rating diagnostic tests". Cancer. 3: 32-35.
Powers, David M W (2011). "Evaluation: From Precision, Recall and F-Score to ROC, Informedness, Markedness and Correlation". Journal of Machine Learning Technologies. 2 (1): 37-63.
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# Two class data("two_class_example") j_index(two_class_example, truth, predicted) # Multiclass library(dplyr) data(hpc_cv) hpc_cv %>% filter(Resample == "Fold01") %>% j_index(obs, pred) # Groups are respected hpc_cv %>% group_by(Resample) %>% j_index(obs, pred) # Weighted macro averaging hpc_cv %>% group_by(Resample) %>% j_index(obs, pred, estimator = "macro_weighted") # Vector version j_index_vec(two_class_example$truth, two_class_example$predicted) # Making Class2 the "relevant" level options(yardstick.event_first = FALSE) j_index_vec(two_class_example$truth, two_class_example$predicted) options(yardstick.event_first = TRUE)
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