Abstract Prediction Object
This is the abstract base class for task objects like PredictionClassif or PredictionRegr .
Prediction objects store the following information:
predict_type. E.g., the class probabilities for classification or the estimated standard error for regression.Note that this object is usually constructed via a derived classes, e.g. PredictionClassif or PredictionRegr .
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.
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html
Package list("mlr3viz") for some generic visualizations.
Extension packages for additional task types:
Other Prediction: PredictionClassif, PredictionRegr
data: (named list())
Internal data structure.
task_type: (character(1))
Required type of the Task .
task_properties: (character())
Required properties of the Task .
predict_types: (character())
Set of predict types this object stores.
man: (character(1))
String in the format `[pkg]::[topic]` pointing to a manual page for this object. Defaults to `NA`, but can be set by child classes.
row_ids: (integer())
Vector of row ids for which predictions are stored.
truth: (any)
True (observed) outcome.
missing: (integer())
Returns `row_ids` for which the predictions are missing or incomplete.
format()Helper for print outputs.
Prediction$format(...)
...: (ignored).
print()Printer.
Prediction$print(...)
...: (ignored).
help()Opens the corresponding help page referenced by field $man.
Prediction$help()
score()Calculates the performance for all provided measures Task and Learner may be NULL for most measures, but some measures need to extract information from these objects. Note that the predict_sets of the measures are ignored by this method, instead all predictions are used.
Prediction$score(
measures = NULL,
task = NULL,
learner = NULL,
train_set = NULL
)
measures: (Measure | list of Measure )
Measure(s) to calculate.
task: (Task ).
learner: (Learner ).
train_set: (integer()).
Prediction .
obs_loss()Calculates the observation-wise loss via the loss function set in the Measure 's field obs_loss. Returns a data.table() with the columns row_ids, truth, response and one additional numeric column for each measure, named with the respective measure id. If there is no observation-wise loss function for the measure, the column is filled with NA values. Note that some measures such as RMSE, do have an $obs_loss, but they require an additional transformation after aggregation, in this example taking the square-root.
Prediction$obs_loss(measures = NULL)
measures: (Measure | list of Measure )
Measure(s) to calculate.
filter()Filters the Prediction , keeping only predictions for the provided row_ids. This changes the object in-place, you need to create a clone to preserve the original Prediction .
Prediction$filter(row_ids)
row_ids: integer()
Row indices.
self, modified.
clone()The objects of this class are cloneable with this method.
Prediction$clone(deep = FALSE)
deep: Whether to make a deep clone.
Useful links