Datasets / scikit-learn / UCI

Breast cancer diagnostic

Cell nucleus measurements with a malignant/benign diagnosis.

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569 rows31 columns scikit-learn / UCI

Worked examples

Each of these is a real run: the pipeline picked the columns, shaped the rows and chose the form. Open the trace to see what it decided and why.

Measurements ranked by separation between diagnoses

The gap between the two group means, divided by their pooled spread - the same effect size a statistician would reach for, so measurements in different units can be ranked together.

which measurements best separate malignant from benign? — 8 rows charted from 569, drawn as a hbar

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How it decided

Finding the data136 ms

Breast cancer diagnostic: 569 rows, 31 columns. all thirty measurements, grouped by diagnosis.

Shaping the data18 ms

569 rows in, 8 out across 2 columns. The gap between the two group means, divided by their pooled spread - the same effect size a statistician would reach for, so measurements in different units can be ranked together.

Choosing the chart9 ms

hbar. A ranking of long-named measurements: horizontal bars.

Applying defaults1 ms

Palette, spacing, axis titles and legend placement applied. 0 issues found.

Mean nucleus radius by diagnosis

Two groups and one measure. Two bars is close to a number, and the chart earns its place only because the gap is the point.

compare mean radius by diagnosis — 2 rows charted from 569, drawn as a bar

Ask this yourself See the working

How it decided

Finding the data763 ms

Breast cancer diagnostic: 569 rows, 31 columns. diagnosis and mean_radius.

Shaping the data6 ms

569 rows in, 2 out across 2 columns. Two groups and one measure. Two bars is close to a number, and the chart earns its place only because the gap is the point.

Choosing the chart5 ms

bar. One measure across two groups: bars.

Applying defaults1 ms

Palette, spacing, axis titles and legend placement applied. 0 issues found.

Schema

31 columns, typed as the pipeline sees them — which is how it knows what can go on a time axis and what can be summed.

Column	Type	Example
mean_radius	number	17.99
mean_texture	number	10.38
mean_perimeter	number	122.8
mean_area	number	1001.0
mean_smoothness	number	0.1184
mean_compactness	number	0.2776
mean_concavity	number	0.3001
mean_concave_points	number	0.1471
mean_symmetry	number	0.2419
mean_fractal_dimension	number	0.0787
radius_error	number	1.095
texture_error	number	0.9053
perimeter_error	number	8.589
area_error	number	153.4
smoothness_error	number	0.0064
compactness_error	number	0.049
concavity_error	number	0.0537
concave_points_error	number	0.0159
symmetry_error	number	0.03
fractal_dimension_error	number	0.0062
worst_radius	number	25.38
worst_texture	number	17.33
worst_perimeter	number	184.6
worst_area	number	2019.0
worst_smoothness	number	0.1622
worst_compactness	number	0.6656
worst_concavity	number	0.7119
worst_concave_points	number	0.2654
worst_symmetry	number	0.4601
worst_fractal_dimension	number	0.1189
diagnosis	text	malignant

First 8 rows

mean_radius	mean_texture	mean_perimeter	mean_area	mean_smoothness	mean_compactness	mean_concavity	mean_concave_points	mean_symmetry	mean_fractal_dimension	radius_error	texture_error	perimeter_error	area_error	smoothness_error	compactness_error	concavity_error	concave_points_error	symmetry_error	fractal_dimension_error	worst_radius	worst_texture	worst_perimeter	worst_area	worst_smoothness	worst_compactness	worst_concavity	worst_concave_points	worst_symmetry	worst_fractal_dimension	diagnosis
17.99	10.38	122.8	1001.0	0.1184	0.2776	0.3001	0.1471	0.2419	0.0787	1.095	0.9053	8.589	153.4	0.0064	0.049	0.0537	0.0159	0.03	0.0062	25.38	17.33	184.6	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.1189	malignant
20.57	17.77	132.9	1326.0	0.0847	0.0786	0.0869	0.0702	0.1812	0.0567	0.5435	0.7339	3.398	74.08	0.0052	0.0131	0.0186	0.0134	0.0139	0.0035	24.99	23.41	158.8	1956.0	0.1238	0.1866	0.2416	0.186	0.275	0.089	malignant
19.69	21.25	130.0	1203.0	0.1096	0.1599	0.1974	0.1279	0.2069	0.06	0.7456	0.7869	4.585	94.03	0.0062	0.0401	0.0383	0.0206	0.0225	0.0046	23.57	25.53	152.5	1709.0	0.1444	0.4245	0.4504	0.243	0.3613	0.0876	malignant
11.42	20.38	77.58	386.1	0.1425	0.2839	0.2414	0.1052	0.2597	0.0974	0.4956	1.156	3.445	27.23	0.0091	0.0746	0.0566	0.0187	0.0596	0.0092	14.91	26.5	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.173	malignant
20.29	14.34	135.1	1297.0	0.1003	0.1328	0.198	0.1043	0.1809	0.0588	0.7572	0.7813	5.438	94.44	0.0115	0.0246	0.0569	0.0188	0.0176	0.0051	22.54	16.67	152.2	1575.0	0.1374	0.205	0.4	0.1625	0.2364	0.0768	malignant
12.45	15.7	82.57	477.1	0.1278	0.17	0.1578	0.0809	0.2087	0.0761	0.3345	0.8902	2.217	27.19	0.0075	0.0335	0.0367	0.0114	0.0216	0.0051	15.47	23.75	103.4	741.6	0.1791	0.5249	0.5355	0.1741	0.3985	0.1244	malignant
18.25	19.98	119.6	1040.0	0.0946	0.109	0.1127	0.074	0.1794	0.0574	0.4467	0.7732	3.18	53.91	0.0043	0.0138	0.0225	0.0104	0.0137	0.0022	22.88	27.66	153.2	1606.0	0.1442	0.2576	0.3784	0.1932	0.3063	0.0837	malignant
13.71	20.83	90.2	577.9	0.1189	0.1645	0.0937	0.0599	0.2196	0.0745	0.5835	1.377	3.856	50.96	0.0088	0.0303	0.0249	0.0145	0.0149	0.0054	17.06	28.14	110.6	897.0	0.1654	0.3682	0.2678	0.1556	0.3196	0.1151	malignant

Ask it something else

which measurements best separate malignant from benign?compare mean radius by diagnosis

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