File size: 3,230 Bytes
988d23c cf10439 988d23c cf10439 988d23c cf10439 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
---
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. |