load_breast_cancer (original) (raw)
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.
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 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.
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 andtarget.
Added in version 0.23.
DESCRstr
The full description of the dataset.
filenamestr
The path to the location of the data.
Added 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.
Added 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) [np.str_('malignant'), np.str_('benign')]