featureDescription.txt

Title: Diagnostic Breast Cancer (WDBC)

Results:

	- predicting field 2, diagnosis: B = benign, M = malignant

Relevant information

	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.
	
Number of instances: 569 

Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)

Attribute information

1) ID number
2) Diagnosis (M = malignant, B = benign)
3-32)

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

	a) radius (mean of distances from center to points on the perimeter)
	b) texture (standard deviation of gray-scale values)
	c) perimeter
	d) area
	e) smoothness (local variation in radius lengths)
	f) compactness (perimeter^2 / area - 1.0)
	g) concavity (severity of concave portions of the contour)
	h) concave points (number of concave portions of the contour)
	i) symmetry 
	j) fractal dimension ("coastline approximation" - 1)


The mean, standard error, and "worst" or largest (mean of the three
largest values) of these features were computed for each image,
resulting in 30 features.  For instance, field 3 is Mean Radius, field
13 is Radius SE, field 23 is Worst Radius.

9. Class distribution: 357 benign, 212 malignant