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#SVM
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different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.

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#SVM
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[default - edit me]
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?

Tags
#SVM
Question
[default - edit me]
Answer
different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.
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1.4. Support Vector Machines — scikit-learn 0.23.2 documentation
where number of dimensions is greater than the number of samples. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. Versatile: <span>different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels. The disadvantages of support vector machines include: If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regulariz

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