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Use Support Vector Machines To Find Complex Decision Boundaries with Scikit-learn

We’ll continue with the iris dataset to implement support vector machines, which can be used to find more complex boundaries for classification or regression problems.

More about Support Vector Machines can be found at scikit-learn.

There are several types of kernel function that can be used with SVMs. Scikit-learn supports these kernels:

-linear

-polynomial ('poly')

-rbf (radial basis function)

-sigmoid

Custom kernels are also supported. Rbf is the default kernel type.

A good overview of kernels can be found here, or at the scikit-learn page.

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