Support Vector Machine (SVM)

SVC parameters

Kernel Parameter

selects the type of hyperplane used to separate the data. Using ‘linear’ will use a linear hyperplane (a line in the case of 2D data). ‘rbf’ and ‘poly’ uses a non-linear hyper-plane.

kernels = [‘linear’, ‘rbf’, ‘poly’]

for kernel in kernels:

svc = svm.SVC(kernel=kernels).fit(X, y)

plotSVC(‘kernel=’ + str(kernel))

Gamma Parameter

gamma is a parameter for nonlinear hyperplanes. The higher the gamma value it tries to exactly fit the training data set

gammas = [0.1, 1, 10, 100]

for gamma in gammas:

svc = svm.SVC(kernel=’rbf’, gamma=gamma).fit(X, y)

plotSVC(‘gamma=’ + str(gamma))

C parameter

C is the penalty parameter of the error term. It controls the trade off between smooth decision boundary and classifying the training points correctly.

cs = [0.1, 1, 10, 100, 1000]

for c in cs:

svc = svm.SVC(kernel=’rbf’, C=c).fit(X, y)

plotSVC(‘C=’ + str(c))

Degree parameter

degree is a parameter used when kernel is set to ‘poly’. It’s basically the degree of the polynomial used to find the hyperplane to split the data.

degrees = [0, 1, 2, 3, 4, 5, 6]

for degree in degrees:

svc = svm.SVC(kernel=’poly’, degree=degree).fit(X, y)

plotSVC(‘degree=’ + str(degree))

Source: In Depth: Parameter tuning for SVC | by Mohtadi Ben Fraj | All things AI | Medium

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