Is MSE a good loss function?
MSE behaves nicely in this case and will converge even with a fixed learning rate. The gradient of MSE loss is high for larger loss values and decreases as loss approaches 0, making it more precise at the end of training (see figure below.)
What is a good MSE loss?
Long answer: the ideal MSE isn’t 0, since then you would have a model that perfectly predicts your training data, but which is very unlikely to perfectly predict any other data. What you want is a balance between overfit (very low MSE for training data) and underfit (very high MSE for test/validation/unseen data).
Why do we use cross entropy loss?
Cross-entropy loss is used when adjusting model weights during training. The aim is to minimize the loss, i.e, the smaller the loss the better the model. A perfect model has a cross-entropy loss of 0.
Why do we take the root of the MSE?
The only issue with MSE is that the order of loss is more than that of the data. As my data is of order 1 and the loss function, MSE has an order of 2. So we cannot directly correlate data with the error. Hence, we take the root of the MSE — which is the Root Mean Squared Error:
Which is more quadratic Huber loss or MSE?
Huber loss is less sensitive or more robust to outliers in data than the MSE. It’s also differentiable at 0. It’s basically absolute error, which becomes quadratic when the error is small.
When to use MSE or Mae in machine learning?
What this equation essentially says is: for loss values less than delta, use the MSE; for loss values greater than delta, use the MAE. This effectively combines the best of both worlds from the two loss functions! Using the MAE for larger loss values mitigates the weight that we put on outliers so that we still get a well-rounded model.
When to use the MSE loss in classification?
Essentially, this means that using the MSE loss makes sense if the assumption that your outputs are a real-valued function of your inputs, with a certain amount of irreducible Gaussian noise, with constant mean and variance. If these assumptions don’t hold true (such as in the context of classification), the MSE loss may not be the best bet.