Dataset research report
Breast Cancer Wisconsin research report
A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.
Executive Summary
Breast Cancer Wisconsin Diagnostic Dataset Following description was retrieved from breast cancer dataset on UCI machine learning repository. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at here. Separating plane described above was obtained using Multisurface Method-Tree (MSM-T), a classification method which uses linear… See the full description on the dataset page: https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin.
Research Context
Breast Cancer Wisconsin: 500 rows by 32 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.
Data Profile
Chart Evidence
These views are generated from the dataset profile. Each chart is paired with a Helix query so it can be opened, adjusted, and exported.
Total radius_mean by diagnosis
Top diagnosis values ranked by summed radius_mean.
Open and export this chartradius_mean vs texture_mean
radius_mean vs texture_mean, coloured by diagnosis.
Open and export this chartCorrelation of numeric columns
Pearson correlation between numeric columns.
Open and export this chartradius_mean by diagnosis
Spread of radius_mean across diagnosis groups.
Open and export this chartFollow-Up Queries
Preview Rows
| # | idinteger | diagnosistext | radius_meanfloat | texture_meanfloat | perimeter_meanfloat | area_meanfloat | smoothness_meanfloat | compactness_meanfloat |
|---|---|---|---|---|---|---|---|---|
| 1 | 842302 | M | 17.99 | 10.38 | 122.8 | 1001 | 0.1184 | 0.2776 |
| 2 | 842517 | M | 20.57 | 17.77 | 132.9 | 1326 | 0.08474 | 0.07864 |
| 3 | 84300903 | M | 19.69 | 21.25 | 130 | 1203 | 0.1096 | 0.1599 |
| 4 | 84348301 | M | 11.42 | 20.38 | 77.58 | 386.1 | 0.1425 | 0.2839 |
| 5 | 84358402 | M | 20.29 | 14.34 | 135.1 | 1297 | 0.1003 | 0.1328 |
| 6 | 843786 | M | 12.45 | 15.7 | 82.57 | 477.1 | 0.1278 | 0.17 |
Data Dictionary
- id numeric
- diagnosis categorical
- radius_mean numeric
- texture_mean numeric
- perimeter_mean numeric
- area_mean numeric
- smoothness_mean numeric
- compactness_mean numeric
- concavity_mean numeric
- concave points_mean numeric
- symmetry_mean numeric
- fractal_dimension_mean numeric
- radius_se numeric
- texture_se numeric
- perimeter_se numeric
- area_se numeric
- smoothness_se numeric
- compactness_se numeric
- concavity_se numeric
- concave points_se numeric
- symmetry_se numeric
- fractal_dimension_se numeric
- radius_worst numeric
- texture_worst numeric
- perimeter_worst numeric
- area_worst numeric
- smoothness_worst numeric
- compactness_worst numeric
- concavity_worst numeric
- concave points_worst numeric
- symmetry_worst numeric
- fractal_dimension_worst numeric
- Unnamed: 32 unknown
Method And Limits
- Load the catalog entry and preview rows from the processed dataset file.
- Infer numeric, categorical, time, and location fields from real columns.
- Generate a small set of defensive Plotly chart specifications from that profile.
- Expose each chart idea as a query link so the report can be rerun or exported in Helix.
This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.