Kernel Trick Can Be Dangerous
Understand the importance of choosing an appropriate kernel and keeping an eye on model complexity.
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The use of the kernel trick seems very effective and efficient. Although not every machine learning algorithm can be reformulated to incorporate kernels, but many popular algorithms that rely on dot products, inner products, or distances can be reformulated to incorporate kernels. However, the use of kernels in machine learning algorithms can become dangerous when the choice of kernel isn’t appropriate for the given problem. For example, using a linear kernel on a highly nonlinear dataset can result in underfitting, while using a polynomial ...