L2 Loss, commonly referred to as Mean Squared Error (MSE), is a popular loss function used in machine learning and statistical modeling to measure the accuracy of a model’s predictions. It quantifies the difference between the actual values and the values predicted by the model.
Mathematically, L2 Loss is calculated by taking the average of the squared differences between each predicted value and the corresponding actual value. The formula is given by:
L2 Loss = (1/n) * Σ(actual – predicted)²
where n is the number of observations, actual is the actual value, and predicted is the predicted value.
The key characteristic of L2 Loss is that it heavily penalizes larger errors due to the squaring of the differences. This property makes it sensitive to outliers, which can significantly affect the overall loss value. As a result, L2 Loss is often used in regression tasks where the goal is to minimize the error between predicted and actual values, leading to more accurate models.
While L2 Loss is widely used, it may not always be the best choice, especially in situations where outliers are present. In such cases, alternative loss functions like L1 Loss (Mean Absolute Error) or Huber Loss may be more appropriate.