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

## Usage

smape(data, ...)

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

smape_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 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(), mpe(), msd(), poisson_log_loss(), rmse(), rpd(), rpiq(), rsq_trad(), rsq()

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

## Author

Max Kuhn, Riaz Hedayati

## Examples

# Supply truth and predictions as bare column names
smape(solubility_test, solubility, prediction)
#> # A tibble: 1 × 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 × 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 × 1
#>   avg_estimate
#>          <dbl>
#> 1         34.3