[amp_mcq option1=”linear kernel” option2=”polynomial kernel” option3=”rbf kernel” option4=”nonlinear kernel” correct=”option4″]<\/p>\n
The correct answer is D. nonlinear kernel.<\/p>\n
A kernel method is a machine learning algorithm that uses a kernel function to map data points into a higher dimensional space, where a linear decision boundary can be found. The most common kernel methods are the linear kernel, the polynomial kernel, and the radial basis function (RBF) kernel.<\/p>\n
The linear kernel is the simplest kernel method. It maps data points into a higher dimensional space by simply taking the dot product of the original data points. This means that the decision boundary in the higher dimensional space is a linear line.<\/p>\n
The polynomial kernel is a more complex kernel method. It maps data points into a higher dimensional space by taking the dot product of the original data points raised to a power. This means that the decision boundary in the higher dimensional space is a polynomial curve.<\/p>\n
The RBF kernel is the most complex kernel method. It maps data points into a higher dimensional space by taking the Gaussian function of the distance between the original data points. This means that the decision boundary in the higher dimensional space is a Gaussian curve.<\/p>\n
A nonlinear kernel is a kernel method that is not one of the three kernel methods described above. There are many different nonlinear kernels, but they are all more complex than the linear, polynomial, and RBF kernels.<\/p>\n
In conclusion, the correct answer is D. nonlinear kernel.<\/p>\n","protected":false},"excerpt":{"rendered":"
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