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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 a data.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. If objects is set to TRUE, the constructed objects are returned in the list column named object.

Examples

as.data.table(mlr_tasks)
#>                key                   label task_type nrow ncol properties lgl
#>  1: boston_housing   Boston Housing Prices      regr  506   18              0
#>  2:  breast_cancer Wisconsin Breast Cancer   classif  683   10   twoclass   0
#>  3:  german_credit           German Credit   classif 1000   21   twoclass   0
#>  4:           iris            Iris Flowers   classif  150    5 multiclass   0
#>  5:         mtcars            Motor Trends      regr   32   11              0
#>  6:       penguins         Palmer Penguins   classif  344    8 multiclass   0
#>  7:           pima    Pima Indian Diabetes   classif  768    9   twoclass   0
#>  8:          sonar  Sonar: Mines vs. Rocks   classif  208   61   twoclass   0
#>  9:           spam       HP Spam Detection   classif 4601   58   twoclass   0
#> 10:           wine            Wine Regions   classif  178   14 multiclass   0
#> 11:            zoo             Zoo Animals   classif  101   17 multiclass  15
#>     int dbl chr fct ord pxc
#>  1:   3  12   0   2   0   0
#>  2:   0   0   0   0   9   0
#>  3:   3   0   0  14   3   0
#>  4:   0   4   0   0   0   0
#>  5:   0  10   0   0   0   0
#>  6:   3   2   0   2   0   0
#>  7:   0   8   0   0   0   0
#>  8:   0  60   0   0   0   0
#>  9:   0  57   0   0   0   0
#> 10:   2  11   0   0   0   0
#> 11:   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
#> 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
#> 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")