Can a validation error be less than a training error?
Generally speaking though, training error will almost always underestimate your validation error. However it is possible for the validation error to be less than the training. You can think of it two ways: Your training set had many ‘hard’ cases to learn. Your validation set had mostly ‘easy’ cases to predict.
Which is the best definition of generalization error?
Generalization error From Wikipedia, the free encyclopedia For supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error or the risk) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data.
How is generalization error minimized in machine learning?
As a result, measurements of prediction error on the current data may not provide much information about predictive ability on new data. Generalization error can be minimized by avoiding overfitting in the learning algorithm.
How is validation error averaged over whole epoch?
Training error is averaged over whole epoch, rather all at once at the end of the epoch, but validation error is only at end of epoch. As we sample our training data to compute gradients, we might as well compute the loss over them as well.
When can validation accuracy be greater than training accuracy?
It is just usual that accuracy via test data (new unseen data for testing performance or validity of proposed model, also called cross validation) may be less than or equal to the accuracy over training data. Can you help by adding an answer? When can Validation Accuracy be greater than Training Accuracy for Deep Learning Models?
How are validation loss and training loss measured?
Training loss is measured during each epoch While validation loss is measured after each epoch Your training loss is continually reported over the course of an entire epoch; however, validation metrics are computed over the validation set only once the current training epoch is completed.
Why are validation sets more accurate than training sets?
Especially if the dataset split is not random (in case where temporal or spatial patterns exist) the validation set may be fundamentally different, i.e less noise or less variance, from the train and thus easier to to predict leading to higher accuracy on the validation set than on training.
When is validation set too small for machine learning?
If the validation set is to small it does not adequately represent the probability distribution of the data. If your training set is small there is not enough data to adequately train the model. Also your model is very basic and may not be adequate to cover the complexity of the data.
When does validation accuracy have a real meaning?
Once that works then you can be confident in the data and build your own model if you wish. Fact is validation loss and accuracy do not have real meaning until your training accuracy gets reasonably high say 85%. Thanks for contributing an answer to Stack Overflow!