Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

mape

Mean absolute percent error

Description

Calculate the mean absolute percentage error. This metric is in relative units.

Usage

mape(data, ...)

## S3 method for class 'data.frame'
mape(data, truth, estimate, na_rm = TRUE, ...)

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

Arguments

data

A data.frame containing the truth and estimate columns.
...

Not currently used.
truth

The column identifier for the true results (that is numeric). 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 numeric vector.
estimate

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

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

Details

Note that a value of Inf is returned for mape() when the observed value is negative.

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 mape_vec(), a single numeric value (or NA).

Author(s)

Max Kuhn

See Also

Other numeric metrics: ccc(), huber_loss_pseudo(), huber_loss(), iic(), mae(), mase(), mpe(), msd(), rmse(), rpd(), rpiq(), rsq_trad(), rsq(), smape()

Other accuracy metrics: ccc(), huber_loss_pseudo(), huber_loss(), iic(), mae(), mase(), mpe(), msd(), rmse(), smape()

Examples

# Supply truth and predictions as bare column names
mape(solubility_test, solubility, prediction)

library(dplyr)

set.seed(1234)
size <- 100
times <- 10

# create 10 resamples
solubility_resampled <- bind_rows(
  replicate(
    n = times,
    expr = sample_n(solubility_test, size, replace = TRUE),
    simplify = FALSE
  ),
  .id = "resample"
)

# Compute the metric by group
metric_results <- solubility_resampled %>%
  group_by(resample) %>%
  mape(solubility, prediction)

metric_results

# Resampled mean estimate
metric_results %>%
  summarise(avg_estimate = mean(.estimate))
yardstick

Tidy Characterizations of Model Performance

v0.0.8
MIT + file LICENSE
Authors
Max Kuhn [aut], Davis Vaughan [aut, cre], RStudio [cph]
Initial release

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.