|
--- |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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dataset_info: |
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features: |
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- name: diagnosis |
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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 |
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|
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## Overview |
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|
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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). |
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|
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## 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. |
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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. |
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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} |
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} |
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``` |
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|
|
- Dataset Name: breast-cancer-wisconsin |
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- Language: English |
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- Total Size: 569 demonstrations |
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|
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## Contents |
|
The dataset consists of a data frame with 30 columns + diagnosis = M (37,3%) and B (62,7%). |
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|
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## How to use |
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|
|
```python |
|
from datasets import load_dataset |
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|
|
dataset = load_dataset("AiresPucrs/breast-cancer-wisconsin", split='train') |
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|
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``` |
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|
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## 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. |
