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://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic

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.

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.

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_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.

DESCRstr

The full description of the dataset.

filenamestr

The path to the location of the data.

(data, target)tuple if return_X_y is True

A tuple of two ndarrays. The first contains a 2D ndarray of shape (569, 30) with each row representing one sample and the columns 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.

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)
[np.str_('malignant'), np.str_('benign')]