sklearn.datasets.load_breast_cancer — scikit-learn 0.20.4 documentation (original) (raw)
sklearn.datasets. load_breast_cancer(return_X_y=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 |
Read more in the User Guide.
| Parameters: | return_X_y : boolean, 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. |
|---|---|
| Returns: | data : Bunch Dictionary-like object, the interesting attributes are: ‘data’, the data to learn, ‘target’, the classification labels, ‘target_names’, the meaning of the labels, ‘feature_names’, the meaning of the features, and ‘DESCR’, the full description of the dataset, ‘filename’, the physical location of breast cancer csv dataset (added in version 0.20). (data, target) : tuple if return_X_y is True New in version 0.18. The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is downloaded from: https://goo.gl/U2Uwz2 |
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']