`sklearn.datasets`.load_breast_cancer¶

sklearn.datasets.load_breast_cancer(*, return_X_y=False, as_frame=False)[source]¶

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://goo.gl/U2Uwz2

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.

New 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.

New in version 0.23.

Returns:

dataBunch

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_nameslist: The names of the dataset columns.
target_nameslist: 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

Permutation Importance with Multicollinear or Correlated Features

Effect of varying threshold for self-training

sklearn.datasets.load_breast_cancer¶

Examples using sklearn.datasets.load_breast_cancer¶

`sklearn.datasets`.load_breast_cancer¶

Examples using `sklearn.datasets.load_breast_cancer`¶