Evaluate forecasts in a Binary Format

Usage

score_binary(data, forecast_unit, metrics)

Arguments

data

A data.frame or data.table with the predictions and observations. For scoring using score(), the following columns need to be present:

true_value - the true observed values
prediction - predictions or predictive samples for one true value. (You only don't need to provide a prediction column if you want to score quantile forecasts in a wide range format.)

For scoring integer and continuous forecasts a sample column is needed:

sample - an index to identify the predictive samples in the prediction column generated by one model for one true value. Only necessary for continuous and integer forecasts, not for binary predictions.

For scoring predictions in a quantile-format forecast you should provide a column called quantile:

quantile: quantile to which the prediction corresponds

In addition a model column is suggested and if not present this will be flagged and added to the input data with all forecasts assigned as an "unspecified model").

You can check the format of your data using check_forecasts() and there are examples for each format (example_quantile, example_continuous, example_integer, and example_binary).

forecast_unit

A character vector with the column names that define the unit of a single forecast, i.e. a forecast was made for a combination of the values in forecast_unit.

metrics

the metrics you want to have in the output. If NULL (the default), all available metrics will be computed. For a list of available metrics see available_metrics(), or check the metrics data set.

Value

A data.table with appropriate scores. For more information see score().

Author

Nikos Bosse nikosbosse@gmail.com