Now you can request additional data and/or customized columns!
Try It Now!- breast-cancer: 14 error(s) found.
Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
3 | 101kB | arff csv zip | 5 years ago | 4 years ago | Open Data Commons Public Domain Dedication and License |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
breast-cancer_arff | 29kB | arff (29kB) | ||
breast-cancer | 19kB | csv (19kB) , json (60kB) | ||
breast-cancer_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 14kB | zip (14kB) |
Signup to Premium Service for additional or customised data - Get Started
This is a preview version. There might be more data in the original version.
Signup to Premium Service for additional or customised data - Get Started
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
age | 1 | string (default) | |
menopause | 2 | string (default) | |
tumor-size | 3 | string (default) | |
inv-nodes | 4 | string (default) | |
node-caps | 5 | string (default) | |
deg-malig | 6 | number (default) | |
breast | 7 | string (default) | |
breast-quad | 8 | string (default) | |
irradiat | 9 | string (default) | |
Class | 10 | string (default) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/machine-learning/breast-cancer
data info machine-learning/breast-cancer
tree machine-learning/breast-cancer
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/breast-cancer/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/machine-learning/breast-cancer/r/0.arff
curl -L https://datahub.io/machine-learning/breast-cancer/r/1.csv
curl -L https://datahub.io/machine-learning/breast-cancer/r/2.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/machine-learning/breast-cancer/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$resources$name)
# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
if(json_data$resources$datahub$type[i]=='derived/csv'){
path_to_file = json_data$resources$path[i]
data <- read.csv(url(path_to_file))
print(data)
}
}
Note: You might need to run the script with root permissions if you are running on Linux machine
Install the Frictionless Data data package library and the pandas itself:
pip install datapackage
pip install pandas
Now you can use the datapackage in the Pandas:
import datapackage
import pandas as pd
data_url = 'https://datahub.io/machine-learning/breast-cancer/datapackage.json'
# to load Data Package into storage
package = datapackage.Package(data_url)
# to load only tabular data
resources = package.resources
for resource in resources:
if resource.tabular:
data = pd.read_csv(resource.descriptor['path'])
print (data)
For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):
pip install datapackage
To get Data Package into your Python environment, run following code:
from datapackage import Package
package = Package('https://datahub.io/machine-learning/breast-cancer/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
print(resource.read())
If you are using JavaScript, please, follow instructions below:
Install data.js
module using npm
:
$ npm install data.js
Once the package is installed, use the following code snippet:
const {Dataset} = require('data.js')
const path = 'https://datahub.io/machine-learning/breast-cancer/datapackage.json'
// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
const dataset = await Dataset.load(path)
// get list of all resources:
for (const id in dataset.resources) {
console.log(dataset.resources[id]._descriptor.name)
}
// get all tabular data(if exists any)
for (const id in dataset.resources) {
if (dataset.resources[id]._descriptor.format === "csv") {
const file = dataset.resources[id]
// Get a raw stream
const stream = await file.stream()
// entire file as a buffer (be careful with large files!)
const buffer = await file.buffer
// print data
stream.pipe(process.stdout)
}
}
})()
The resources for this dataset can be found at https://www.openml.org/d/13
Author:
Source: Unknown -
Please cite:
Citation Request: This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. Please include this citation if you plan to use this database.
Title: Breast cancer data (Michalski has used this)
Sources: – Matjaz Zwitter & Milan Soklic (physicians) Institute of Oncology University Medical Center Ljubljana, Yugoslavia – Donors: Ming Tan and Jeff Schlimmer ([email protected]) – Date: 11 July 1988
Past Usage: (Several: here are some) – Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045, Philadelphia, PA: Morgan Kaufmann. – accuracy range: 66%-72% – Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In Progress in Machine Learning (from the Proceedings of the 2nd European Working Session on Learning), 11-30, Bled, Yugoslavia: Sigma Press. – 8 test results given: 65%-72% accuracy range – Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning, 121-134, Ann Arbor, MI. – 4 systems tested: accuracy range was 68%-73.5% – Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press. – Assistant-86: 78% accuracy
Relevant Information: This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also lymphography and primary-tumor.)
This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.
Number of Instances: 286
Number of Attributes: 9 + the class attribute
Attribute Information:
irradiat: yes, no.
Missing Attribute Values: (denoted by “?”) Attribute #: Number of instances with missing values: 6. 8 9. 1.
Class Distribution:
Num Instances: 286 Num Attributes: 10 Num Continuous: 0 (Int 0 / Real 0) Num Discrete: 10 Missing values: 9 / 0.3%
name type enum ints real missing distinct (1)
1 ‘age’ Enum 100% 0% 0% 0 / 0% 6 / 2% 0% 2 ‘menopause’ Enum 100% 0% 0% 0 / 0% 3 / 1% 0% 3 ‘tumor-size’ Enum 100% 0% 0% 0 / 0% 11 / 4% 0% 4 ‘inv-nodes’ Enum 100% 0% 0% 0 / 0% 7 / 2% 0% 5 ‘node-caps’ Enum 97% 0% 0% 8 / 3% 2 / 1% 0% 6 ‘deg-malig’ Enum 100% 0% 0% 0 / 0% 3 / 1% 0% 7 ‘breast’ Enum 100% 0% 0% 0 / 0% 2 / 1% 0% 8 ‘breast-quad’ Enum 100% 0% 0% 1 / 0% 5 / 2% 0% 9 ‘irradiat’ Enum 100% 0% 0% 0 / 0% 2 / 1% 0% 10 ‘Class’ Enum 100% 0% 0% 0 / 0% 2 / 1% 0%
Notifications of data updates and schema changes
Warranty / guaranteed updates
Workflow integration (e.g. Python packages, NPM packages)
Customized data (e.g. you need different or additional data)
Or suggest your own feature from the link below