La pérdida L2, comúnmente conocida como Error cuadrático medio (MSE), is a popular función de pérdida utilizado en aprendizaje automático and modelado estadístico to measure the accuracy of a model’s predictions. It quantifies the difference between the actual values and the values predicted by the model.
Matemáticamente, la pérdida L2 se calcula tomando el promedio de las diferencias al cuadrado entre cada valor predicho y el valor real correspondiente. La fórmula es la siguiente:
L2 Loss = (1/n) * Σ(actual – predicted)²
where n is the number of observations, actual is the actual value, and predicted es el valor predicho.
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 pérdida total 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 (Error Absoluto Medio) o la pérdida Huber pueden ser más apropiadas.