View source: R/num-poisson_log_loss.R
poisson_log_loss | R Documentation |
Calculate the loss function for the Poisson distribution.
poisson_log_loss(data, ...)
## S3 method for class 'data.frame'
poisson_log_loss(data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
poisson_log_loss_vec(truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
data |
A |
... |
Not currently used. |
truth |
The column identifier for the true counts (that is |
estimate |
The column identifier for the predicted
results (that is also |
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 poisson_log_loss_vec()
, a single numeric
value (or NA
).
Max Kuhn
Other numeric metrics:
ccc()
,
huber_loss_pseudo()
,
huber_loss()
,
iic()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
rmse()
,
rpd()
,
rpiq()
,
rsq_trad()
,
rsq()
,
smape()
Other accuracy metrics:
ccc()
,
huber_loss_pseudo()
,
huber_loss()
,
iic()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
rmse()
,
smape()
count_truth <- c(2L, 7L, 1L, 1L, 0L, 3L)
count_pred <- c(2.14, 5.35, 1.65, 1.56, 1.3, 2.71)
count_results <- dplyr::tibble(count = count_truth, pred = count_pred)
# Supply truth and predictions as bare column names
poisson_log_loss(count_results, count, pred)
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