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Provides an interface to 'OpenML.org' to list and download machine learning data and tasks. Data and tasks can be automatically converted to 'mlr3' tasks. For a more sophisticated interface which also allows uploading experiments, see the 'OpenML' package.

mlr3 Integration

This package adds the mlr3::Task "oml" and the mlr3::Resampling "oml" to mlr3::mlr_tasks and mlr3::mlr_resamplings, respectively. For the former you may pass either a data_id or a task_id, the latter requires a task_id. Furthermore it allows to convert the OpenML objects to mlr3 objects using the usual S3 generics such as mlr3::as_task, mlr3::as_learner, mlr3::as_resampling, mlr3::as_resample_result, mlr3::as_benchmark_result or mlr3::as_data_backend. This allows for a frictionless integration of OpenML and mlr3.

Options

  • mlr3oml.cache: Enables or disables caching globally. If set to FALSE, caching is disabled. If set to TRUE, cache directory as reported by R_user_dir() is used. Alternatively, you can specify a path on the local file system here. Default is FALSE.

  • mlr3oml.api_key: API key to use. All operations supported by this package work without an API key, but you might get rate limited without an API key. If not set, defaults to the value of the environment variable OPENMLAPIKEY.

  • mlr3oml.arff_parser: ARFF parser to use, defaults to the internal one relies on data.table::fread(). Can also be set to "RWeka" for the parser in RWeka or "farff" for the reader implemented in farff.

  • mlr3oml.server: Address for the server that should be used. Can be the test server or the usual OpenML server.

Logging

The lgr package is used for logging. To change the threshold, use lgr::get_logger("mlr3oml")$set_threshold().

Author

Maintainer: Michel Lang michellang@gmail.com (ORCID)