breast_cancer#

The data set contains features for binary prediction of breast cancer.

The data has 569 patient records with 30 features and one binary target column, referring to the presence of breast cancer in the patient.

This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets. https://goo.gl/U2Uwz2

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.

Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, “Decision Tree Construction Via Linear Programming.” Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.

The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [ K. P. Bennett and O. L. Mangasarian: “Robust Linear Programming Discrimination of Two Linearly Inseparable Sets”, Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/

References:

W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993.
O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995.
W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 163-171.

The typical ML task in this dataset is to build a model that classifies between benign and malignant samples.

Ten real-valued features are computed for each cell nucleus:

radius (mean of distances from center to points on the perimeter)
texture (standard deviation of gray-scale values)
perimeter
area
smoothness (local variation in radius lengths)
compactness (perimeter^2 / area - 1.0)
concavity (severity of concave portions of the contour)
concave points (number of concave portions of the contour)
symmetry
fractal dimension (“coastline approximation” - 1)

Dataset Shape:

Dataset Shape#
Property	Value
Samples Total	569
Dimensionality	30
Features	real
Targets	boolean

Description:

Dataset Description#
mean radius	Feature	mean radius
mean texture	Feature	mean texture
mean perimeter	Feature	mean perimeter
mean area	Feature	mean area
mean smoothness	Feature	mean smoothness
mean compactness	Feature	mean compactness
mean concavity	Feature	mean concavity
mean concave points	Feature	mean concave points
mean symmetry	Feature	mean symmetry
mean fractal dimension	Feature	mean fractal dimension
radius error	Feature	radius error
texture error	Feature	texture error
perimeter error	Feature	perimeter error
area error	Feature	area error
smoothness error	Feature	smoothness error
compactness error	Feature	compactness error
concavity error	Feature	concavity error
concave points error	Feature	concave points error
symmetry error	Feature	symmetry error
fractal dimension error	Feature	fractal dimension error
worst radius	Feature	worst radius
worst texture	Feature	worst texture
worst perimeter	Feature	worst perimeter
worst area	Feature	worst area
worst smoothness	Feature	worst smoothness
worst compactness	Feature	worst compactness
worst concavity	Feature	worst concavity
worst concave points	Feature	worst concave points
worst symmetry	Feature	worst symmetry
worst fractal dimension	Feature	worst fractal dimension
target	Label	The class (Benign, Malignant)

Functions

`load_data`([data_format, as_train_test])	Load and returns the Breast Cancer dataset (classification).
`load_fitted_model`([pretrained])	Load and return a fitted classification model to predict the flower type in the iris dataset.

classification

load_data