load_linnerud#

sklearn.datasets.load_linnerud(*, return_X_y=False, as_frame=False)[source]#

Load and return the physical exercise Linnerud dataset.

This dataset is suitable for multi-output regression tasks.

Samples total	20
Dimensionality	3 (for both data and target)
Features	integer
Targets	integer

Read more in the User Guide.

Parameters:

return_X_ybool, default=False: If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target objects.

Added in version 0.18.
as_framebool, default=False: If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.

Added in version 0.23.

Returns:

dataBunch

Dictionary-like object, with the following attributes.

data{ndarray, dataframe} of shape (20, 3): The data matrix. If as_frame=True, data will be a pandas DataFrame.
target: {ndarray, dataframe} of shape (20, 3): The regression targets. If as_frame=True, target will be a pandas DataFrame.
feature_names: list: The names of the dataset columns.
target_names: list: The names of the target columns.
frame: DataFrame of shape (20, 6): Only present when as_frame=True. DataFrame with data and target.

Added in version 0.23.
DESCR: str: The full description of the dataset.
data_filename: str: The path to the location of the data.
target_filename: str: The path to the location of the target.

Added in version 0.20.

(data, target)tuple if return_X_y is True

A tuple of two ndarrays. The first contains a 2D ndarray of shape (20, 3) with each row representing one sample and the columns representing the features. The second ndarray of shape (20, 3) contains the multi target samples. If as_frame=True, both arrays are pandas dataframes.

Added in version 0.18.

Examples

>>> from sklearn.datasets import load_linnerud
>>> linnerud = load_linnerud()
>>> linnerud.data.shape
(20, 3)
>>> linnerud.target.shape
(20, 3)