yardstick: huber_loss_pseudo – R documentation

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huber_loss_pseudo

Psuedo-Huber Loss

Description

Calculate the Pseudo-Huber Loss, a smooth approximation of huber_loss(). Like huber_loss(), this is less sensitive to outliers than rmse().

Usage

huber_loss_pseudo(data, ...)

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

huber_loss_pseudo_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_pseudo_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.

Hartley, Richard (2004). Multiple View Geometry in Computer Vision. (Second Edition). Page 619.

Examples

# Supply truth and predictions as bare column names
huber_loss_pseudo(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_pseudo(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