Squared loss

Squared loss is a loss function that can be used in the learning setting in which we are predicting a real-valued variable y given an input variable x.

1. How do you calculate squared loss?
2. Is L2 loss same as MSE loss?
3. Why is a loss divided by 2?
4. Is squared error loss random?

How do you calculate squared loss?

How do you calculate mean squared error loss? Mean squared error (MSE) loss is calculated by taking the difference between `y` and our prediction, then square those values. We take these new numbers (square them), add all of that together to get a final value, finally divide this number by y again.

Is L2 loss same as MSE loss?

Is L2 loss the same as MSE (Mean of Squared Errors)? L2 loss and MSE are related, but not the same. L2 loss is the loss for each example, whilst MSE is the cost function which is an aggregation of all the loss values in the dataset.

Why is a loss divided by 2?

It is simple. It is because when you take the derivative of the cost function, that is used in updating the parameters during gradient descent, that 2 in the power get cancelled with the 12 multiplier, thus the derivation is cleaner.

Is squared error loss random?

MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate.