Suppose you have a dataset with m = 50m=50 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?

Step-by-step explanation:

Consider X to be the matrix whose columns are the values for our 50 examples. The normal equation gives us the values of in the following way

The matrix however, might not be invertible when . So we must use the pseudo inverse to solve the problem. For a big number of features, calculating the pseudoinverse might be computational expensive. So, gradient descent should be prefered.

you can't solve x because it is 2 linear equation and you only have one equation