Suppose we are doing ordinary least-squares linear regression with a fictitious dimension. Which of the following changes can never make the cost function’s value on the training data smaller? A: Discard the fictitious dimension (i.e., don’t append a 1 to every sample point). B: Append quadratic features to each sample point. C: Project the sample points onto a lower-dimensional subspace with PCA (without changing the labels) and perform regression on the projected points. D: Center the design matrix (so each feature has mean zero).

Suppose we are doing ordinary least-squares linear regression with a fictitious dimension. Which of the following changes can never make the cost function’s value on the training data smaller? A: Discard the fictitious dimension (i.e., don’t append a 1 to every sample point). B: Append quadratic features to each sample point. C: Project the sample points onto a lower-dimensional subspace with PCA (without changing the labels) and perform regression on the projected points. D: Center the design matrix (so each feature has mean zero).

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