A simple mlr3misc::Dictionary storing objects of class Task.
Each task has an associated help page, see mlr_tasks_[id].
This dictionary can get populated with additional tasks by add-on packages, e.g. mlr3data, mlr3proba or mlr3cluster. mlr3oml allows to interact with OpenML.
For a more convenient way to retrieve and construct tasks, see tsk()/tsks().
Format
R6::R6Class object inheriting from mlr3misc::Dictionary.
Methods
See mlr3misc::Dictionary.
S3 methods
as.data.table(dict, ..., objects = FALSE)
mlr3misc::Dictionary ->data.table::data.table()
Returns adata.table::data.table()with columns "key", "label", "task_type", "nrow", "ncol", "properties", and the number of features of type "lgl", "int", "dbl", "chr", "fct" and "ord", respectively. Ifobjectsis set toTRUE, the constructed objects are returned in the list column namedobject.
See also
Sugar functions: tsk(), tsks()
Extension Packages: mlr3data
Other Dictionary:
mlr_learners,
mlr_measures,
mlr_resamplings,
mlr_task_generators
Other Task:
Task,
TaskClassif,
TaskRegr,
TaskSupervised,
TaskUnsupervised,
mlr_tasks_boston_housing,
mlr_tasks_breast_cancer,
mlr_tasks_german_credit,
mlr_tasks_iris,
mlr_tasks_mtcars,
mlr_tasks_penguins,
mlr_tasks_pima,
mlr_tasks_sonar,
mlr_tasks_spam,
mlr_tasks_wine,
mlr_tasks_zoo
Examples
as.data.table(mlr_tasks)
#> Key: <key>
#> key label task_type nrow ncol properties
#> <char> <char> <char> <int> <int> <list>
#> 1: boston_housing Boston Housing Prices regr 506 18
#> 2: breast_cancer Wisconsin Breast Cancer classif 683 10 twoclass
#> 3: german_credit German Credit classif 1000 21 twoclass
#> 4: iris Iris Flowers classif 150 5 multiclass
#> 5: mtcars Motor Trends regr 32 11
#> 6: penguins Palmer Penguins classif 344 8 multiclass
#> 7: pima Pima Indian Diabetes classif 768 9 twoclass
#> 8: sonar Sonar: Mines vs. Rocks classif 208 61 twoclass
#> 9: spam HP Spam Detection classif 4601 58 twoclass
#> 10: wine Wine Regions classif 178 14 multiclass
#> 11: zoo Zoo Animals classif 101 17 multiclass
#> lgl int dbl chr fct ord pxc
#> <int> <int> <int> <int> <int> <int> <int>
#> 1: 0 3 12 0 2 0 0
#> 2: 0 0 0 0 0 9 0
#> 3: 0 3 0 0 14 3 0
#> 4: 0 0 4 0 0 0 0
#> 5: 0 0 10 0 0 0 0
#> 6: 0 3 2 0 2 0 0
#> 7: 0 0 8 0 0 0 0
#> 8: 0 0 60 0 0 0 0
#> 9: 0 0 57 0 0 0 0
#> 10: 0 2 11 0 0 0 0
#> 11: 15 1 0 0 0 0 0
task = mlr_tasks$get("penguins") # same as tsk("penguins")
head(task$data())
#> species bill_depth bill_length body_mass flipper_length island sex
#> <fctr> <num> <num> <int> <int> <fctr> <fctr>
#> 1: Adelie 18.7 39.1 3750 181 Torgersen male
#> 2: Adelie 17.4 39.5 3800 186 Torgersen female
#> 3: Adelie 18.0 40.3 3250 195 Torgersen female
#> 4: Adelie NA NA NA NA Torgersen <NA>
#> 5: Adelie 19.3 36.7 3450 193 Torgersen female
#> 6: Adelie 20.6 39.3 3650 190 Torgersen male
#> year
#> <int>
#> 1: 2007
#> 2: 2007
#> 3: 2007
#> 4: 2007
#> 5: 2007
#> 6: 2007
# Add a new task, based on a subset of penguins:
data = palmerpenguins::penguins
data$species = factor(ifelse(data$species == "Adelie", "1", "0"))
task = TaskClassif$new("penguins.binary", data, target = "species", positive = "1")
# add to dictionary
mlr_tasks$add("penguins.binary", task)
# list available tasks
mlr_tasks$keys()
#> [1] "boston_housing" "breast_cancer" "german_credit" "iris"
#> [5] "mtcars" "penguins" "penguins.binary" "pima"
#> [9] "sonar" "spam" "wine" "zoo"
# retrieve from dictionary
mlr_tasks$get("penguins.binary")
#> <TaskClassif:penguins.binary> (344 x 8)
#> * Target: species
#> * Properties: twoclass
#> * Features (7):
#> - int (3): body_mass_g, flipper_length_mm, year
#> - dbl (2): bill_depth_mm, bill_length_mm
#> - fct (2): island, sex
# remove task again
mlr_tasks$remove("penguins.binary")