yardstick: huber_loss – R documentation

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huber_loss

Huber loss

Description

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, ...)

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).

Author(s)

James Blair

References

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

Examples

# Supply truth and predictions as bare column names
huber_loss(solubility_test, solubility, prediction)

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

# Resampled mean estimate
metric_results %>%
  summarise(avg_estimate = mean(.estimate))

yardstick

Tidy Characterizations of Model Performance

v0.0.8

MIT + file LICENSE

Authors

Max Kuhn [aut], Davis Vaughan [aut, cre], RStudio [cph]

Initial release