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Mean log loss for Poisson data

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

Calculate the loss function for the Poisson distribution.

Usage

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, ...)

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 counts (that is integer). 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, an integer 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.

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 poisson_log_loss_vec(), a single numeric value (or NA).

See also

Other numeric metrics: ccc(), huber_loss(), huber_loss_pseudo(), iic(), mae(), mape(), mase(), mpe(), msd(), rmse(), rpd(), rpiq(), rsq(), rsq_trad(), smape()

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

Author

Max Kuhn

Examples

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)
#> # A tibble: 1 × 3
#>   .metric          .estimator .estimate
#>   <chr>            <chr>          <dbl>
#> 1 poisson_log_loss standard        1.42