View source: R/class-accuracy.R
accuracy | R Documentation |
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, case_weights = NULL, ...)
accuracy_vec(truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
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 |
case_weights |
The optional column identifier for case weights.
This should be an unquoted column name that evaluates to a numeric column
in |
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
Other class metrics:
bal_accuracy()
,
detection_prevalence()
,
f_meas()
,
j_index()
,
kap()
,
mcc()
,
npv()
,
ppv()
,
precision()
,
recall()
,
sens()
,
spec()
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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