Среднеквадратичная ошибка (MSE)

The average loss per example when L~2~ loss is used. Calculate Mean Squared Error as follows: 1. Calculate the L~2~ loss for a batch. 2. Divide the L~2~ loss by the number of examples in the batch. where: - $n$ is the number of examples. - $y$ is the actual value of the label. - $\\\\y\$ is the model's prediction for $y$. For example, consider the loss on the following batch of five examples: Model's prediction Squared loss | ------| 6 1 | 4 1 | 11 9 | 6 4 | 8 1 | Therefore, the Mean Squared Error is: Mean Squared Error is a popular training optimizer, particularly for linear regression. Contrast Mean Squared Error with Mean Absolute Error and Root Mean Squared Error. TensorFlow Playground uses Mean Squared Error to calculate loss values. Outliers strongly influence Mean Squared Error. For example, a loss of 1 is a squared loss of 1, but a loss of 3 is a squared loss of 9. In the preceding table, the example with a loss of 3 accounts for \\~56% of the Mean Squared Error, while each of the examples with a loss of 1 accounts for only 6% of the Mean Squared Error. Outliers don't influence Mean Absolute Error as strongly as Mean Squared Error. For example, a loss of 3 accounts for only \\~38% of the Mean Absolute Error. Clipping is one way to prevent extreme outliers from damaging your model's predictive ability.