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# `sklearn.datasets`.load_breast_cancer

sklearn.datasets.load_breast_cancer(*, return_X_y=False, as_frame=False)

  Load and return the breast cancer wisconsin dataset (classification).

  The breast cancer dataset is a classic and very easy binary classification dataset.

  Classes | 2
  Samples per class | 212(M),357(B)
  Samples total | 569
  Dimensionality | 30
  Features | real, positive

  The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is downloaded from: https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic

  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.

  Returns:
    data `Bunch`

      Dictionary-like object, with the following attributes.

      data{ndarray, dataframe} of shape (569, 30)

        The data matrix. If `as_frame=True`, `data` will be a pandas DataFrame.

      target{ndarray, Series} of shape (569,)

        The classification target. If `as_frame=True`, `target` will be a pandas Series.

      feature_namesndarray of shape (30,)

        The names of the dataset columns.

      target_namesndarray of shape (2,)

        The names of target classes.

      frameDataFrame of shape (569, 31)

        Only present when `as_frame=True`. DataFrame with `data` and `target`.

        New in version 0.23.

      DESCRstr

        The full description of the dataset.

      filenamestr

        The path to the location of the data.

        New in version 0.20.

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

      A tuple of two ndarrays by default. The first contains a 2D ndarray of shape (569, 30) with each row representing one sample and each column representing the features. The second ndarray of shape (569,) contains the target samples. If `as_frame=True`, both arrays are pandas objects, i.e. `X` a dataframe and `y` a series.

      New in version 0.18.

  Examples

  Let’s say you are interested in the samples 10, 50, and 85, and want to know their class name.

      >>> from sklearn.datasets import load_breast_cancer
      >>> data = load_breast_cancer()
      >>> data.target[[10, 50, 85]]
      array([0, 1, 0])
      >>> list(data.target_names)
      ['malignant', 'benign']

## Examples using `sklearn.datasets.load_breast_cancer`

Post pruning decision trees with cost complexity pruning

Post pruning decision trees with cost complexity pruning

Model-based and sequential feature selection

Model-based and sequential feature selection

Permutation Importance with Multicollinear or Correlated Features

Permutation Importance with Multicollinear or Correlated Features

Effect of varying threshold for self-training

Effect of varying threshold for self-training