---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: diagnosis
    dtype: string
  - name: radius_mean
    dtype: float64
  - name: texture_mean
    dtype: float64
  - name: perimeter_mean
    dtype: float64
  - name: area_mean
    dtype: float64
  - name: smoothness_mean
    dtype: float64
  - name: compactness_mean
    dtype: float64
  - name: concavity_mean
    dtype: float64
  - name: concave points_mean
    dtype: float64
  - name: symmetry_mean
    dtype: float64
  - name: fractal_dimension_mean
    dtype: float64
  - name: radius_se
    dtype: float64
  - name: texture_se
    dtype: float64
  - name: perimeter_se
    dtype: float64
  - name: area_se
    dtype: float64
  - name: smoothness_se
    dtype: float64
  - name: compactness_se
    dtype: float64
  - name: concavity_se
    dtype: float64
  - name: concave points_se
    dtype: float64
  - name: symmetry_se
    dtype: float64
  - name: fractal_dimension_se
    dtype: float64
  - name: radius_worst
    dtype: float64
  - name: texture_worst
    dtype: float64
  - name: perimeter_worst
    dtype: float64
  - name: area_worst
    dtype: float64
  - name: smoothness_worst
    dtype: float64
  - name: compactness_worst
    dtype: float64
  - name: concavity_worst
    dtype: float64
  - name: concave points_worst
    dtype: float64
  - name: symmetry_worst
    dtype: float64
  - name: fractal_dimension_worst
    dtype: float64
  splits:
  - name: train
    num_bytes: 139405
    num_examples: 569
  download_size: 141996
  dataset_size: 139405
license: cc
language:
- en
pretty_name: breast-cancer-wisconsin
size_categories:
- n<1K
---
# breast-cancer-wisconsin

## Overview 

The dataset contains features computed from digitized images of breast cancer biopsies, which are used to predict 
whether a breast mass is benign (non-cancerous) or malignant (cancerous).

## Dataset Details

The original dataset is the [Breast Cancer Wisconsin (Diagnostic)](https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic). This file concerns credit card applications. 
The dataset is based on features computed from digitized images of breast mass tissue samples. These features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. 
Based on these features, the goal is to predict whether the mass is benign or malignant.

```latex
@inproceedings{Street1993NuclearFE,
  title={Nuclear feature extraction for breast tumor diagnosis},
  author={William Nick Street and William H. Wolberg and Olvi L. Mangasarian},
  booktitle={Electronic imaging},
  year={1993},
  url={https://api.semanticscholar.org/CorpusID:14922543}
}
```

- Dataset Name: breast-cancer-wisconsin
- Language:  English
- Total Size: 569 demonstrations

## Contents
The dataset consists of a data frame with 30 columns + diagnosis = M (37,3%) and B (62,7%).


## How to use

```python
from datasets import load_dataset

dataset = load_dataset("AiresPucrs/breast-cancer-wisconsin", split='train')

```

## License

This dataset is licensed under a [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0) license.