skdatasets.repositories.keel.fetch

skdatasets.repositories.keel.fetch(collection: str, name: str, data_home: Optional[str] = None, nfolds: Literal[None, 1, 5, 10] = None, dobscv: bool = False, *, return_X_y: Literal[False] = False) → Bunch[source]
skdatasets.repositories.keel.fetch(collection: str, name: str, data_home: Optional[str] = None, nfolds: Literal[None, 1, 5, 10] = None, dobscv: bool = False, *, return_X_y: Literal[True]) → Tuple[ndarray[Any, dtype[float]], ndarray[Any, dtype[Union[int, float]]]]

Fetch Keel dataset.

Fetch a Keel dataset by collection and name. More info at http://sci2s.ugr.es/keel.

Parameters:

  • collection (string) – Collection name.

  • name (string) – Dataset name.

  • data_home (string or None, default None) – Specify another download and cache folder for the data sets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.

  • nfolds (int, default=None) – Number of folds. Depending on the dataset, valid values are {None, 1, 5, 10}.

  • dobscv (bool, default=False) – If folds are in {5, 10}, indicates that the cv folds are distribution optimally balanced stratified. Only available for some datasets.

  • return_X_y (bool, default=False) – If True, returns (data, target) instead of a Bunch object.

  • kwargs (dict) – Optional key-value arguments

Returns:

  • data (Bunch) – Dictionary-like object with all the data and metadata.

  • (data, target) (tuple if return_X_y is True)