Mean Squared Error (MSE)

Used for regression models (quantitative responses)

MSE is small if predicted responses are close to true responses, and large if predicted and true responses differ greatly. Test MSE is very large if the model overfits to the training data.
Good performance requires low variance and low squared bias. Models usually increase one and decrease the other, which is known as the bias-variance trade-off.

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