load_diabetes (original) (raw)
sklearn.datasets.load_diabetes(*, return_X_y=False, as_frame=False, scaled=True)[source]#
Load and return the diabetes dataset (regression).
Note
The meaning of each feature (i.e. feature_names) might be unclear (especially for ltg) as the documentation of the original dataset is not explicit. We provide information that seems correct in regard with the scientific literature in this field of research.
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 object.
Added in version 0.18.
as_framebool, default=False
If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). 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.
scaledbool, default=True
If True, the feature variables are mean centered and scaled by the standard deviation times the square root of n_samples. If False, raw data is returned for the feature variables.
Added in version 1.1.
Returns:
dataBunch
Dictionary-like object, with the following attributes.
data{ndarray, dataframe} of shape (442, 10)
The data matrix. If as_frame=True, data will be a pandas DataFrame.
target: {ndarray, Series} of shape (442,)
The regression target. If as_frame=True, target will be a pandas Series.
feature_names: list
The names of the dataset columns.
frame: DataFrame of shape (442, 11)
Only present when as_frame=True. DataFrame with data andtarget.
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.
**(data, target)**tuple if return_X_y is True
Returns a tuple of two ndarray of shape (n_samples, n_features) A 2D array with each row representing one sample and each column representing the features and/or target of a given sample.
Added in version 0.18.
Examples
from sklearn.datasets import load_diabetes diabetes = load_diabetes() diabetes.target[:3] array([151., 75., 141.]) diabetes.data.shape (442, 10)