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Huber loss

Source: R/num-huber_loss.R
huber_loss.Rd

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.

Usage

huber_loss(data, ...)

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

huber_loss_vec(
  truth,
  estimate,
  delta = 1,
  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.

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.

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

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

Author

James Blair

Examples

# Supply truth and predictions as bare column names
huber_loss(solubility_test, solubility, prediction)
#> # A tibble: 1 × 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 × 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 × 1
#>   avg_estimate
#>          <dbl>
#> 1        0.228