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

mape(data, ...)

# S3 method for 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.

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(), rmse(), rpd(), rpiq(), rsq_trad(), rsq(), smape()

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

Examples

# Supply truth and predictions as bare column names mape(solubility_test, solubility, prediction)
#> # A tibble: 1 x 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 x 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 x 1 #> avg_estimate #> <dbl> #> 1 Inf