This is the class for collections (previously known as studies) served on
A collection can either be a task collection
or run collection.
This object can also be constructed using the sugar function
A run collection contains runs, flows, datasets and tasks.
The primary object are the runs (
The the flows, datasets and tasks are those used in the runs.
A task collection (
main_entity_type = "task") contains tasks and datasets.
The primary object are the tasks (
The datasets are those used in the tasks.
Note: All Benchmark Suites on OpenML are also collections.
Because collections on OpenML can be modified (ids can be added), it is not possible to cache this object.
Vanschoren J, van Rijn JN, Bischl B, Torgo L (2014). “OpenML.” ACM SIGKDD Explorations Newsletter, 15(2), 49--60. doi:10.1145/2641190.2641198 .
Colllection description (meta information), downloaded and converted from the JSON API response.
Whether to use parquet.
The main entity type, either
An vector containing the flow ids of the collection.
An vector containing the data ids of the collection.
An vector containing the run ids of the collection.
An vector containing the task ids of the collection.
Creates a new instance of this R6 class.
OMLCollection$new(id, test_server = test_server_default())
Prints the object.
# For technical reasons, examples cannot be included in this R package. # Instead, these are some relevant resources: # # Large-Scale Benchmarking chapter in the mlr3book: # https://mlr3book.mlr-org.com/chapters/chapter11/large-scale_benchmarking.html # # Package Article: # https://mlr3oml.mlr-org.com/articles/tutorial.html