This is the result container object returned by resample().

Note that all stored objects are accessed by reference. Do not modify any object without cloning it first.

Format

R6::R6Class object.

Construction

rr = ResampleResult$new(data)

Fields

  • data :: data.table::data.table()
    Internal data storage. We discourage users to directly work with this field.

  • task :: Task
    The task resample() operated on.

  • learners :: list of Learner
    List of trained learners, sorted by resampling iteration.

  • resampling :: Resampling
    Instantiated Resampling object which stores the splits into training and test.

  • predictions :: list of Prediction
    List of prediction objects, sorted by resampling iteration.

  • prediction :: Prediction
    Combined Prediction of all individual resampling iterations. Note that the performance of measures is not calculated on this object, but instead on each iterations separately and then combined with an aggregate function.

  • warnings :: data.table::data.table()
    Returns a table with all warning messages. Column names are "iteration" and "msg". Note that there can be multiple rows per resampling iteration if multiple warnings have been recorded.

  • errors :: data.table::data.table()
    Returns a table with all error messages. Column names are "iteration" and "msg". Note that there can be multiple rows per resampling iteration if multiple errors have been recorded.

  • hash :: character(1)
    Hash (unique identifier) for this object.

Methods

  • performance(measures = NULL, ids = TRUE)
    (list of Measure, logical(1)) -> data.table::data.table()
    Returns a table with one row for each resampling iteration, including all involved objects: Task, Learner, Resampling, iteration number (integer(1)), and Prediction. A column with the individual (per resampling iteration) performance is added for each Measure, named with the id of the respective measure. If ids is TRUE, extra columns with the ids of objects ("task_id", "learner_id", "resampling_id") are binded to the table to allow a more convenient subsetting. If measures is NULL, measures defaults to the return value of default_measures().

  • aggregate(measures = NULL)
    list of Measure -> named numeric()
    Calculates and aggregates performance values for all provided measures, according to the respective aggregation function in Measure. If measures is NULL, measures defaults to the return value of default_measures().

S3 Methods

Examples

task = tsk("iris") learner = lrn("classif.rpart") resampling = rsmp("cv", folds = 3) rr = resample(task, learner, resampling) print(rr)
#> <ResampleResult> of 3 iterations #> * Task: iris #> * Learner: classif.rpart #> * Performance: 0.060 [classif.ce] #> * Warnings: 0 in 0 iterations #> * Errors: 0 in 0 iterations
rr$aggregate(msr("classif.acc"))
#> classif.acc #> 0.94
rr$prediction
#> <PredictionClassif> for 150 observations: #> row_id truth response #> 3 setosa setosa #> 7 setosa setosa #> 16 setosa setosa #> --- #> 131 virginica virginica #> 133 virginica virginica #> 149 virginica virginica
rr$prediction$confusion
#> truth #> response setosa versicolor virginica #> setosa 50 0 0 #> versicolor 0 46 5 #> virginica 0 4 45
rr$warnings
#> Empty data.table (0 rows and 2 cols): iteration,msg
rr$errors
#> Empty data.table (0 rows and 2 cols): iteration,msg