README.md

CANCER CLASSIFICATION PROJECT

SVM

• can be used for both classifications & regression

Attributes

['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']

Label / The prediction

• classes = ['malignant', 'benign']

Requirements

1. sklearn

   import sklearn 
   from sklearn import datasets
   from sklearn import svm

HOW SUPPORT VECTOR MACHINES works!!!

• SVM create a hyperplane, that divides your data

CASE 1:

• The hyperplane is the same distance from the two closest data points from both classes (red & green) in this case.

a.

NOTE: Meaning we could generate multiple hyperplanes eg;

b.

c.

• The best hyperplane to use is the one where the distance between our classes is the greatest. In our case The best to use is a..

CASE 2:

• if we get this type of data what happens? kernel trick

• In this case we use kernel trick

• Kernel, simply take the x¹ 's and x² of each data point and comes up with an x³. For 3D

• This shows how a 2D is converted to a 3D.

CASE 3:

• Using a soft margin

  model = svm.SVC(kernel="linear", C=1)

• If this occurs like in our case where by a RED DATA POINT has strayed. We ignore it to get a better result.

• a few points maybe ignored

NOTE: In a hard margin this is not allowed.