View source: R/get_-functions.R
get_forecast_type | R Documentation |
Helper function to infer the forecast type based on a data.frame or similar with forecasts and observed values. See the details section below for information on the different forecast types.
get_forecast_type(data)
data |
A data.frame (or similar) with predicted and observed values.
See the details section of |
Character vector of length one with either "binary", "quantile", "sample" or "point".
Various different forecast types / forecast formats are supported. At the moment, those are:
point forecasts
binary forecasts ("soft binary classification")
nominal forecasts ("soft classification with multiple unordered classes")
Probabilistic forecasts in a quantile-based format (a forecast is represented as a set of predictive quantiles)
Probabilistic forecasts in a sample-based format (a forecast is represented as a set of predictive samples)
Forecast types are determined based on the columns present in the input data. Here is an overview of the required format for each forecast type:
All forecast types require a data.frame or similar with columns observed
predicted
, and model
.
Point forecasts require a column observed
of type numeric and a column
predicted
of type numeric.
Binary forecasts require a column observed
of type factor with exactly
two levels and a column predicted
of type numeric with probabilities,
corresponding to the probability that observed
is equal to the second
factor level. See details here for more information.
Nominal forecasts require a column observed
of type factor with N levels,
(where N is the number of possible outcomes), a column predicted
of type
numeric with probabilities (which sum to one across all possible outcomes),
and a column predicted_label
of type factor with N levels, denoting the
outcome for which a probability is given. Forecasts must be complete, i.e.
there must be a probability assigned to every possible outcome.
Quantile-based forecasts require a column observed
of type numeric,
a column predicted
of type numeric, and a column quantile_level
of type
numeric with quantile-levels (between 0 and 1).
Sample-based forecasts require a column observed
of type numeric,
a column predicted
of type numeric, and a column sample_id
of type
numeric with sample indices.
For more information see the vignettes and the example data
(example_quantile, example_sample_continuous, example_sample_discrete,
example_point()
, example_binary, and example_nominal).
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