Gamma controls how far the influence of a single training example reaches, which in turn effects how tightly the decision boundaries end up surrounding points in the input space 

Small gamma: larger similarity radius, points farther apart are considered similar, more points grouped together, smoother decision values, more regularization 

Large gamma: kernel value decays more quickly, points have to be very close to be considered similar, results in more complex tightly constrained decision boundaries 

Increase gamma → sharpening kernel 


Gamma 

Gamma varies between 0.0001 - 10
C varies between 0.1 - 100


University of Michigan - Ann Arbor

This function creates a support vector classifier (SVC) object. Two important parameters are:
- C: Regularization parameter. In other words, this controls the cost of misclassification on the training data.
- kernel: specifies the kernel type to be used in the algorithm. This parameter can be set to "linear", "poly", "rbf", "sigmoid", or "precomputed"

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