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

smape(data, ...)

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

smape_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 smape_vec(), a single numeric value (or NA).

Details

This implementation of smape() is the "usual definition" where the denominator is divided by two.

See also

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

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

Examples

# Supply truth and predictions as bare column names smape(solubility_test, solubility, prediction)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 smape standard 36.7
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) %>% smape(solubility, prediction) metric_results
#> # A tibble: 10 x 4 #> resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 1 smape standard 33.5 #> 2 10 smape standard 28.9 #> 3 2 smape standard 41.6 #> 4 3 smape standard 28.4 #> 5 4 smape standard 37.0 #> 6 5 smape standard 31.8 #> 7 6 smape standard 48.3 #> 8 7 smape standard 27.8 #> 9 8 smape standard 35.6 #> 10 9 smape standard 30.2
# Resampled mean estimate metric_results %>% summarise(avg_estimate = mean(.estimate))
#> # A tibble: 1 x 1 #> avg_estimate #> <dbl> #> 1 34.3