When would you use categorical cross-entropy loss vs mean squared error loss?

Cross-entropy loss, or log loss, measure the performance of a classification model whose output is a probability value between 0 and 1. It is preferred for classification, while mean squared error (MSE) is one of the best choices for regression. This comes directly from the statement of your problems itself.

Why you should use cross-entropy error instead of classification error or mean squared error for neural network classifier training?

When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network. This NN also has a classification error of 1/3 = 0.33.

Which is better MSE or RMSE?

The MSE has the units squared of whatever is plotted on the vertical axis. The RMSE is directly interpretable in terms of measurement units, and so is a better measure of goodness of fit than a correlation coefficient. One can compare the RMSE to observed variation in measurements of a typical point.

Which loss is best for regression?

Regression loss

1. Mean Square Error, Quadratic loss, L2 Loss. Mean Square Error (MSE) is the most commonly used regression loss function.
2. Mean Absolute Error, L1 Loss. Mean Absolute Error (MAE) is another loss function used for regression models.
3. Huber Loss, Smooth Mean Absolute Error.
4. Log-Cosh Loss.
5. Quantile Loss.

Can cross entropy loss be greater than 1?

Mathematically speaking, if your label is 1 and your predicted probability is low (like 0.1), the cross entropy can be greater than 1, like losses.

What is a good MSE?

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).

How do you interpret cross-entropy losses?

Cross-entropy loss increases as the predicted probability diverges from the actual label. So predicting a probability of . 012 when the actual observation label is 1 would be bad and result in a high loss value. A perfect model would have a log loss of 0.

When would you use categorical cross entropy loss vs mean squared error loss?

Cross-entropy loss, or log loss, measure the performance of a classification model whose output is a probability value between 0 and 1. It is preferred for classification, while mean squared error (MSE) is one of the best choices for regression. This comes directly from the statement of your problems itself.

What is BCE loss used for?

BCE loss is used for the binary classification tasks. If you are using BCE loss function, you just need one output node to classify the data into two classes. The output value should be passed through a sigmoid activation function and the range of output is (0 – 1).

When to use binary cross entropy in autoencoder?

I am working on an autoencoder for non-binary data ranging in [0,1] and while I was exploring existing solutions I noticed that many people (e.g., the keras tutorial on autoencoders, this guy) use binary cross-entropy as the loss function in this scenario.

When to use binary cross entropy or mean squared error?

The issue is, when I use the binary cross-entropy as loss function, the loss value for training and testing is relatively high as compared to using the mean squared error (MSE) function. Upon research, I came across justifications that binary cross-entropy should be used for classification problems and MSE for the regression problem.

Why is binary crossentropy used as the loss function?

Closed 12 months ago. I was wondering why binary crossentropy can be used as the loss function in autoencoders trained on (normalized) images, e.g. here or this paper?

Why do we prefer binary crossentropy over MSE?

One thing I would like to add is why one would prefer binary crossentropy over MSE. Normally, the activation function of the last layer is sigmoid, which can lead to loss saturation (“plateau”). This saturation could prevent gradient-based learning algorithms from making progress.