Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.datasets`.load_diabetes

sklearn.datasets.load_diabetes(*, return_X_y=False, as_frame=False, scaled=True)

  Load and return the diabetes dataset (regression).

  Samples total | 442
  Dimensionality | 10
  Features | real, -.2 < x < .2
  Targets | integer 25 - 346

  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_y bool, default=False

      If True, returns `(data, target)` instead of a Bunch object. See below for more information about the `data` and `target` object.

      New in version 0.18.

    as_frame bool, 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.

      New in version 0.23.

    scaled bool, 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.

      New in version 1.1.

  Returns:
    data `Bunch`

      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` and `target`.

        New 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.

      New in version 0.18.

## Examples using `sklearn.datasets.load_diabetes`

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2

Gradient Boosting regression

Gradient Boosting regression

Plot individual and voting regression predictions

Plot individual and voting regression predictions

Model Complexity Influence

Model Complexity Influence

Model-based and sequential feature selection

Model-based and sequential feature selection

Lasso and Elastic Net

Lasso and Elastic Net

Lasso model selection via information criteria

Lasso model selection via information criteria

Lasso model selection: AIC-BIC / cross-validation

Lasso model selection: AIC-BIC / cross-validation

Lasso path using LARS

Lasso path using LARS

Linear Regression Example

Linear Regression Example

Sparsity Example: Fitting only features 1 and 2

Sparsity Example: Fitting only features 1 and 2

Advanced Plotting With Partial Dependence

Advanced Plotting With Partial Dependence

Imputing missing values before building an estimator

Imputing missing values before building an estimator

Plotting Cross-Validated Predictions

Plotting Cross-Validated Predictions

Cross-validation on diabetes Dataset Exercise

Cross-validation on diabetes Dataset Exercise