Mean absolute percent error

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

Usage

mape(data, ...)

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

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

Arguments

data

A data.frame containing the columns specified by the truth and estimate arguments.

...

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.

case_weights

The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For _vec() functions, a numeric vector, hardhat::importance_weights(), or hardhat::frequency_weights().

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).

Details

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

See also

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

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

Author

Max Kuhn

Examples

# Supply truth and predictions as bare column names
mape(solubility_test, solubility, prediction)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 mape    standard         Inf

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
#> # A tibble: 10 × 4
#>    resample .metric .estimator .estimate
#>    <chr>    <chr>   <chr>          <dbl>
#>  1 1        mape    standard       Inf  
#>  2 10       mape    standard       Inf  
#>  3 2        mape    standard       Inf  
#>  4 3        mape    standard        28.5
#>  5 4        mape    standard        95.3
#>  6 5        mape    standard       Inf  
#>  7 6        mape    standard        73.1
#>  8 7        mape    standard       Inf  
#>  9 8        mape    standard       Inf  
#> 10 9        mape    standard        37.9

# Resampled mean estimate
metric_results %>%
  summarise(avg_estimate = mean(.estimate))
#> # A tibble: 1 × 1
#>   avg_estimate
#>          <dbl>
#> 1          Inf