This is the abstract base class for task objects like PredictionClassif or PredictionRegr.
Prediction objects store the following information:
The row ids of the test set
The corresponding true (observed) response.
The corresponding predicted response.
Additional predictions based on the class and predict_type.
E.g., the class probabilities for classification or the estimated standard error for regression.
R6::R6Class object.
This object is constructed via a derived classes, e.g. PredictionClassif or PredictionRegr.
row_ids :: (integer() | character())
Vector of row ids for which predictions are stored.
truth :: any
True (observed) outcome.
task_type :: character(1)
Stores the type of the Task.
predict_types :: character()
Vector of predict types this object stores.
missing :: logical()
Returns row_ids for which the predictions are missing or incomplete.
score(measures = NULL, task = NULL, learner = NULL)
(list() of Measure, Task, Learner) -> Prediction
Calculates the performance for all provided measures
If no measure is provided, defaults to the measure defined in mlr_reflections$default_measures
(mlr_measures_classif.ce for classification and mlr_measures_regr.mse for regression).
Task and Learner may be NULL for many measures, but some measures need to extract information
from these objects.
as.data.table(rr)
Prediction -> data.table::data.table()
Converts the data to a data.table::data.table().
c(..., keep_duplicates = TRUE)
(Prediction, Prediction, ...) -> Prediction
Combines multiple Predictions to a single Prediction.
If keep_duplicates is FALSE and there are duplicated row ids,
the data of the former passed objects get overwritten by the data of the later passed objects.
Other Prediction: PredictionClassif,
PredictionRegr