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, ...)
data | A |
---|---|
... | Not currently used. |
truth | The column identifier for the true results
(that is |
estimate | The column identifier for the predicted
results (that is also |
na_rm | A |
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
).
Note that a value of Inf
is returned for mape()
when the
observed value is negative.
Other numeric metrics:
ccc()
,
huber_loss_pseudo()
,
huber_loss()
,
iic()
,
mae()
,
mase()
,
mpe()
,
rmse()
,
rpd()
,
rpiq()
,
rsq_trad()
,
rsq()
,
smape()
Other accuracy metrics:
ccc()
,
huber_loss_pseudo()
,
huber_loss()
,
iic()
,
mae()
,
mase()
,
mpe()
,
rmse()
,
smape()
Max Kuhn
# 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 Inflibrary(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#> # A tibble: 1 x 1 #> avg_estimate #> <dbl> #> 1 Inf