Suppose you have a dataset with m = 1000000m=1000000 examples and n = 200000n=200000 features for each example. You want to use multivariate linear regression to fit the parameters \thetaθ to our data. Should you prefer gradient descent or the normal equation? A. The normal equation, since gradient descent might be unable to find the optimal \thetaθ.

B. Gradient descent, since it will always converge to the optimal \thetaθ.

C. The normal equation, since it provides an efficient way to directly find the solution.

D. Gradient descent, since (X^TX)^{-1}(XTX)-1 will be very slow to compute in the normal equation.

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