Description: The Mean Squared Error (MSE) is a widely used loss function in regression tasks that measures the average difference between the values predicted by a model and the actual observed values. It is calculated by taking the mean of the squares of the differences between predictions and actual values. This metric is particularly valuable because it penalizes larger errors more severely, meaning that a model that makes significant errors in its predictions will be penalized more than one that makes smaller errors. MSE is easy to interpret, as it is expressed in the same units as the output variable, making it easier to evaluate model performance. Additionally, its derivative is continuous and differentiable, making it an ideal choice for optimization algorithms that require gradient calculations, such as gradient descent. In summary, MSE not only provides a quantitative measure of model performance but also guides the training process by allowing adjustments to model parameters to minimize prediction error.
