Squared error loss is a commonly used loss function in statistical inference and decision theory, defined as the square of the difference between the predicted value and the actual value. This loss function emphasizes larger errors more than smaller ones due to the squaring operation, making it particularly sensitive to outliers. It plays a significant role in various statistical modeling techniques, especially in regression analysis where the goal is to minimize the total squared error across all predictions.
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