Technology
Mean Squared Error (MSE) as a Loss Function
Mean squared error (MSE) is the most common loss function in regression: it averages the square of the gap between each true value and its prediction. Squaring punishes big mistakes hard, keeps the function differentiable and gives gradient descent a clear, smooth target to minimise while training a model.