Calculate the root mean squared error. rmse()
is a metric that is in
the same units as the original data.
rmse(data, ...) # S3 method for data.frame rmse(data, truth, estimate, na_rm = TRUE, ...) rmse_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 rmse_vec()
, a single numeric
value (or NA
).
Other numeric metrics:
ccc()
,
huber_loss_pseudo()
,
huber_loss()
,
iic()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
poisson_log_loss()
,
rpd()
,
rpiq()
,
rsq_trad()
,
rsq()
,
smape()
Other accuracy metrics:
ccc()
,
huber_loss_pseudo()
,
huber_loss()
,
iic()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
poisson_log_loss()
,
smape()
Max Kuhn
# Supply truth and predictions as bare column names rmse(solubility_test, solubility, prediction) #> # A tibble: 1 × 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 rmse standard 0.722 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) %>% rmse(solubility, prediction) metric_results #> # A tibble: 10 × 4 #> resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 1 rmse standard 0.715 #> 2 10 rmse standard 0.673 #> 3 2 rmse standard 0.716 #> 4 3 rmse standard 0.644 #> 5 4 rmse standard 0.737 #> 6 5 rmse standard 0.675 #> 7 6 rmse standard 0.807 #> 8 7 rmse standard 0.801 #> 9 8 rmse standard 0.635 #> 10 9 rmse standard 0.692 # Resampled mean estimate metric_results %>% summarise(avg_estimate = mean(.estimate)) #> # A tibble: 1 × 1 #> avg_estimate #> <dbl> #> 1 0.710