12 Questions
A kernel is a dissimilarity metric between vectors.
False
The Gram matrix of dot-products between vectors must be negative semidefinite to be valid.
False
Complex kernels allow us to define simple explicit feature expansions.
False
The Gaussian kernel has a finite number of dimensions.
False
The Taylor series of exp(#) is a polynomial weighted by constants.
True
High degree polynomials are preferred over lower degree polynomials in Gaussian kernels.
False
Kernels allow for expressing a wide variety of ideas of similarity in models.
True
Kernel ridge regression uses a fixed-size kernel across the space.
True
In K nearest neighbors regression, only the least similar units are considered.
False
Type-I and Type-II errors are typically considered equally important in decision-making processes.
False
We must know the risk score a-priori before estimating it.
False
Kernel ridge regression is particularly designed to have local variation properties.
False
Recap on kernels in machine learning focusing on similarity metrics between vectors and complex implicit feature expansions. Understanding the Gram matrix and positive semidefinite properties. Exploring complex kernels and the application of Gaussian kernels.
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