Accuracy
Accuracy is the proportion of the data that are predicted correctly.
accuracy(data, ...) ## S3 method for class 'data.frame' accuracy(data, truth, estimate, na_rm = TRUE, ...) accuracy_vec(truth, estimate, na_rm = TRUE, ...)
data |
Either a |
... |
Not currently used. |
truth |
The column identifier for the true class results
(that is a |
estimate |
The column identifier for the predicted class
results (that is also |
na_rm |
A |
A tibble
with columns .metric
, .estimator
,
and .estimate
and 1 row of values.
For grouped data frames, the number of rows returned will be the same as the number of groups.
For accuracy_vec()
, a single numeric
value (or NA
).
Accuracy extends naturally to multiclass scenarios. Because of this, macro and micro averaging are not implemented.
Max Kuhn
library(dplyr) data("two_class_example") data("hpc_cv") # Two class accuracy(two_class_example, truth, predicted) # Multiclass # accuracy() has a natural multiclass extension hpc_cv %>% filter(Resample == "Fold01") %>% accuracy(obs, pred) # Groups are respected hpc_cv %>% group_by(Resample) %>% accuracy(obs, pred)
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