accuracy

0th

Percentile

Accuracy

Accuracy is the proportion of the data that are predicted correctly.

Usage
accuracy(data, ...)

# S3 method for data.frame accuracy(data, truth, estimate, na_rm = TRUE, ...)

accuracy_vec(truth, estimate, na_rm = TRUE, ...)

Arguments
data

Either a data.frame containing the truth and estimate columns, or a table/matrix where the true class results should be in the columns of the table.

...

Not currently used.

truth

The column identifier for the true class results (that is a factor). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a factor vector.

estimate

The column identifier for the predicted class results (that is also factor). As with truth this can be specified different ways but the primary method is to use an unquoted variable name. For _vec() functions, a factor vector.

na_rm

A logical value indicating whether NA values should be stripped before the computation proceeds.

Value

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).

Multiclass

Accuracy extends naturally to multiclass scenarios. Because of this, macro and micro averaging are not implemented.

See Also

Other class metrics: bal_accuracy, detection_prevalence, f_meas, j_index, kap, mcc, npv, ppv, precision, recall, sens, spec

Aliases
  • accuracy
  • accuracy.data.frame
  • accuracy_vec
Examples
# NOT RUN {
# Two class
data("two_class_example")
accuracy(two_class_example, truth, predicted)

# Multiclass
library(dplyr)
data(hpc_cv)

hpc_cv %>%
  filter(Resample == "Fold01") %>%
  accuracy(obs, pred)

# Groups are respected
hpc_cv %>%
  group_by(Resample) %>%
  accuracy(obs, pred)

# Weighted macro averaging
hpc_cv %>%
  group_by(Resample) %>%
  accuracy(obs, pred, estimator = "macro_weighted")

# Vector version
accuracy_vec(two_class_example$truth, two_class_example$predicted)

# Making Class2 the "relevant" level
options(yardstick.event_first = FALSE)
accuracy_vec(two_class_example$truth, two_class_example$predicted)
options(yardstick.event_first = TRUE)

# }
Documentation reproduced from package yardstick, version 0.0.4, License: GPL-2

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