Calculate the Huber loss, a loss function used in robust regression. This loss function is less sensitive to outliers than rmse(). This function is quadratic for small residual values and linear for large residual values.

huber_loss(data, ...)

# S3 method for data.frame
huber_loss(data, truth, estimate, delta = 1, na_rm = TRUE, ...)

huber_loss_vec(truth, estimate, delta = 1, 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.

delta

A single numeric value. Defines the boundary where the loss function transitions from quadratic to linear. Defaults to 1.

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 huber_loss_vec(), a single numeric value (or NA).

References

Huber, P. (1964). Robust Estimation of a Location Parameter. Annals of Statistics, 53 (1), 73-101.

See also

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

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

Examples

# Supply truth and predictions as bare column names huber_loss(solubility_test, solubility, prediction)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 huber_loss standard 0.234
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) %>% huber_loss(solubility, prediction) metric_results
#> # A tibble: 10 x 4 #> resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 1 huber_loss standard 0.215 #> 2 10 huber_loss standard 0.212 #> 3 2 huber_loss standard 0.229 #> 4 3 huber_loss standard 0.197 #> 5 4 huber_loss standard 0.249 #> 6 5 huber_loss standard 0.208 #> 7 6 huber_loss standard 0.293 #> 8 7 huber_loss standard 0.268 #> 9 8 huber_loss standard 0.190 #> 10 9 huber_loss standard 0.218
# Resampled mean estimate metric_results %>% summarise(avg_estimate = mean(.estimate))
#> # A tibble: 1 x 1 #> avg_estimate #> <dbl> #> 1 0.228