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Can a test error be higher than a training error?
You description is confusing, but it is totally possible to have test error both lower and higher than training error. A lower training error is expected when a method easily overfits to the training data, yet, poorly generalizes.
How to prevent model errors in machine learning?
Since the consequences are often dire, I’m going to discuss how to prevent mistakes in model validation and the necessary components of a correct validation. To kick off the discussion, let’s get grounded in some of the basic concepts of validating machine learning models: predictive modeling, training error, test error and cross validation
Can a validation loss be higher than a training loss?
Simply put, if training loss and validation loss are computed correctly, it is impossible for training loss to be higher than validation loss. This is because back-propagation DIRECTLY reduces error computed on the training set and only INDIRECTLY (not even guaranteed!) reduces error computed on the validation set.
How is classification error calculated in machine learning?
It is now relatively easy to calculate how often our predictions are wrong by comparing the predictions in p to the true values in y – this is called the classification error.
How to test a model for classification error?
Test Error: We get this by using two completely disjoint datasets: one to train the model and the other to calculate the classification error. Both datasets need to have values for y.
Where does the model validation error come from?
Figure 2: The test error comes from using two disjoint datasets: one to train the model and a separate one to calculate the classification error. Calculating any form of error rate for a predictive model is called model validation.
What’s the difference between testing and test errors?
Testing is the process that happens while the program is in development. And a test error is when the program does not behave in accordance with the requirements. Once a program has been tested and is critical, it may undergo formal validation testing.