a) Build a kernel classifier using • the squared error loss function
• an `2 regularizer with λ = 0.5.
• the Guassian Kernel K(u,v) = exp(−||u − v||2/(2σ2).
b) Train your classifier choosing for different values of σ and create a plot with σ on the horizontal axis and accuracy on the vertical axis and comment on the plot. Does your classifier achieve 0% training error?
c) Find a more realistic estimate of the accuracy of your classifier by using 8-fold cross validation. Can you achieve perfect test accuracy?
2. Kernel Regression, Lake Mendota Clarity. The Secchi depth is a measure of water clarity obtained by lowering a black and white disk off the shady side of a boat and recording the depth at which the disk is no longer visible.
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λ = 0.01 and scale parameter σ = 10. Plot the resulting fit, and comment on the results. Do these parameters overfit or underfit the data? Adjust the regularization parameter to find a visually better fit.
b) Describe how you could use k-fold cross validation to systematically find a good value of σ and λ.
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