Suppose fi is a model that optimally fits the data (x, y), and f is another...

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Suppose fi is a model that optimally fits the data (x, y), and f₂ is another model that optimally fits the data (X2, y), where X₂ are the quadratic features of X. Then the loss function value obtained by f2 is always going to be at least equal to that for f₁. Try to come up with a solid mathematical argument that justifies this claim. [Note: as with anything else, feel free to discuss this on Canvas.] Suppose fi is a model that optimally fits the data (x, y), and f₂ is another model that optimally fits the data (X2, y), where X₂ are the quadratic features of X. Then the loss function value obtained by f2 is always going to be at least equal to that for f₁. Try to come up with a solid mathematical argument that justifies this claim. [Note: as with anything else, feel free to discuss this on Canvas.]

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The statement youre asking about relates to the concept of model complexity and how it impacts the loss function value Lets provide a solid mathematic... View the full answer