View source: R/num-huber_loss.R
huber_loss | R Documentation |
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 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,
...
)
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 |
delta |
A single |
na_rm |
A |
case_weights |
The optional column identifier for case weights. This
should be an unquoted column name that evaluates to a numeric column in
|
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
).
James Blair
Huber, P. (1964). Robust Estimation of a Location Parameter. Annals of Statistics, 53 (1), 73-101.
Other numeric metrics:
ccc()
,
huber_loss_pseudo()
,
iic()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
poisson_log_loss()
,
rmse()
,
rpd()
,
rpiq()
,
rsq_trad()
,
rsq()
,
smape()
Other accuracy metrics:
ccc()
,
huber_loss_pseudo()
,
iic()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
poisson_log_loss()
,
rmse()
,
smape()
# 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))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.