Contents
Can validation error be smaller than 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.
Can testing accuracy be higher than training accuracy?
2 Answers. Test accuracy should not be higher than train since the model is optimized for the latter. Even so there would need to be some element of “test data distribution is not the same as that of train” for the observed behavior to occur.
Why is test error smaller than training error?
If your test error is less than the training error, this means that there is a sampling bias in your test. This does not mean that you know the whole subject, just that the test was ‘biased’ for you.
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.
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
What are the concepts of validating machine learning models?
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 What models can be validated? Let’s make sure that we are on the same page and quickly define what we mean by a “predictive model.”
Why is the training set similar to the validation set?
Reason 2: the model is near perfect. Reason 3: the training set is very similar to the validation set, e.g. if some data from the validation set have leaked into the training set: Reason 4: if using a neural network, the training have been prematurely stopped: