#' @field predict_sets (`character()`)\cr
#' During [resample()]/[benchmark()], a [Learner] can predict on multiple sets.
#' Per default, a learner only predicts observations in the test set (`predict_sets == "test"`).
#' To change this behavior, set `predict_sets` to a non-empty subset of `{"train", "test", "internal_valid"}`.
#' The `"train"` predict set contains the train ids from the resampling. This means that if a learner does validation and
#' sets `$validate` to a ratio (creating the validation data from the training data), the train predictions
#' will include the predictions for the validation data.
#' Each set yields a separate [Prediction] object.
#' Those can be combined via getters in [ResampleResult]/[BenchmarkResult], or [Measure]s can be configured
#' to operate on specific subsets of the calculated prediction sets.
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